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Model-based Clustering with Missing Not At Random Data (2112.10425v4)
Published 20 Dec 2021 in stat.ML and cs.LG
Abstract: Model-based unsupervised learning, as any learning task, stalls as soon as missing data occurs. This is even more true when the missing data are informative, or said missing not at random (MNAR). In this paper, we propose model-based clustering algorithms designed to handle very general types of missing data, including MNAR data. To do so, we introduce a mixture model for different types of data (continuous, count, categorical and mixed) to jointly model the data distribution and the MNAR mechanism, remaining vigilant to the relative degrees of freedom of each. Several MNAR models are discussed, for which the cause of the missingness can depend on both the values of the missing variable themselves and on the class membership. However, we focus on a specific MNAR model, called MNARz, for which the missingness only depends on the class membership. We first underline its ease of estimation, by showing that the statistical inference can be carried out on the data matrix concatenated with the missing mask considering finally a standard MAR mechanism. Consequently, we propose to perform clustering using the Expectation Maximization algorithm, specially developed for this simplified reinterpretation. Finally, we assess the numerical performances of the proposed methods on synthetic data and on the real medical registry TraumaBase as well.
- Bouveyron, C., Celeux, G., Murphy, T.B., Raftery, A.E.: Model-based Clustering and Classification for Data Science: with Applications in R. Cambridge University Press, ??? (2019) Bouveyron et al. [2007] Bouveyron, C., Girard, S., Schmid, C.: High-dimensional data clustering. Computational Statistics & Data Analysis 52(1), 502–519 (2007) Bouveyron and Brunet-Saumard [2014] Bouveyron, C., Brunet-Saumard, C.: Model-based clustering of high-dimensional data: A review. Computational Statistics & Data Analysis 71, 52–78 (2014) Marbac et al. [2017] Marbac, M., Biernacki, C., Vandewalle, V.: Model-based clustering of gaussian copulas for mixed data. Communications in Statistics-Theory and Methods (2017) Ramoni et al. [2002] Ramoni, M., Sebastiani, P., Cohen, P.: Bayesian clustering by dynamics. Machine learning 47(1), 91–121 (2002) Xiong and Yeung [2004] Xiong, Y., Yeung, D.-Y.: Time series clustering with arma mixtures. Pattern Recognition 37(8), 1675–1689 (2004) Little and Rubin [2019] Little, R.J., Rubin, D.B.: Statistical Analysis with Missing Data, (2019) Dempster et al. [1977] Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society (1977) Rubin [1976] Rubin, D.B.: Inference and missing data. Biometrika 63(3), 581–592 (1976) Ibrahim et al. [2001] Ibrahim, J.G., Chen, M.-H., Lipsitz, S.R.: Missing responses in generalised linear mixed models when the missing data mechanism is nonignorable. Biometrika (2001) Mohan et al. [2018] Mohan, K., Thoemmes, F., Pearl, J.: Estimation with incomplete data: The linear case. In: IJCAI, pp. 5082–5088 (2018) Hunt and Jorgensen [2003] Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Bouveyron, C., Girard, S., Schmid, C.: High-dimensional data clustering. Computational Statistics & Data Analysis 52(1), 502–519 (2007) Bouveyron and Brunet-Saumard [2014] Bouveyron, C., Brunet-Saumard, C.: Model-based clustering of high-dimensional data: A review. Computational Statistics & Data Analysis 71, 52–78 (2014) Marbac et al. [2017] Marbac, M., Biernacki, C., Vandewalle, V.: Model-based clustering of gaussian copulas for mixed data. Communications in Statistics-Theory and Methods (2017) Ramoni et al. [2002] Ramoni, M., Sebastiani, P., Cohen, P.: Bayesian clustering by dynamics. Machine learning 47(1), 91–121 (2002) Xiong and Yeung [2004] Xiong, Y., Yeung, D.-Y.: Time series clustering with arma mixtures. Pattern Recognition 37(8), 1675–1689 (2004) Little and Rubin [2019] Little, R.J., Rubin, D.B.: Statistical Analysis with Missing Data, (2019) Dempster et al. [1977] Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society (1977) Rubin [1976] Rubin, D.B.: Inference and missing data. Biometrika 63(3), 581–592 (1976) Ibrahim et al. [2001] Ibrahim, J.G., Chen, M.-H., Lipsitz, S.R.: Missing responses in generalised linear mixed models when the missing data mechanism is nonignorable. Biometrika (2001) Mohan et al. [2018] Mohan, K., Thoemmes, F., Pearl, J.: Estimation with incomplete data: The linear case. In: IJCAI, pp. 5082–5088 (2018) Hunt and Jorgensen [2003] Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Bouveyron, C., Brunet-Saumard, C.: Model-based clustering of high-dimensional data: A review. Computational Statistics & Data Analysis 71, 52–78 (2014) Marbac et al. [2017] Marbac, M., Biernacki, C., Vandewalle, V.: Model-based clustering of gaussian copulas for mixed data. Communications in Statistics-Theory and Methods (2017) Ramoni et al. [2002] Ramoni, M., Sebastiani, P., Cohen, P.: Bayesian clustering by dynamics. Machine learning 47(1), 91–121 (2002) Xiong and Yeung [2004] Xiong, Y., Yeung, D.-Y.: Time series clustering with arma mixtures. Pattern Recognition 37(8), 1675–1689 (2004) Little and Rubin [2019] Little, R.J., Rubin, D.B.: Statistical Analysis with Missing Data, (2019) Dempster et al. [1977] Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society (1977) Rubin [1976] Rubin, D.B.: Inference and missing data. Biometrika 63(3), 581–592 (1976) Ibrahim et al. [2001] Ibrahim, J.G., Chen, M.-H., Lipsitz, S.R.: Missing responses in generalised linear mixed models when the missing data mechanism is nonignorable. Biometrika (2001) Mohan et al. [2018] Mohan, K., Thoemmes, F., Pearl, J.: Estimation with incomplete data: The linear case. In: IJCAI, pp. 5082–5088 (2018) Hunt and Jorgensen [2003] Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Marbac, M., Biernacki, C., Vandewalle, V.: Model-based clustering of gaussian copulas for mixed data. Communications in Statistics-Theory and Methods (2017) Ramoni et al. [2002] Ramoni, M., Sebastiani, P., Cohen, P.: Bayesian clustering by dynamics. Machine learning 47(1), 91–121 (2002) Xiong and Yeung [2004] Xiong, Y., Yeung, D.-Y.: Time series clustering with arma mixtures. Pattern Recognition 37(8), 1675–1689 (2004) Little and Rubin [2019] Little, R.J., Rubin, D.B.: Statistical Analysis with Missing Data, (2019) Dempster et al. [1977] Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society (1977) Rubin [1976] Rubin, D.B.: Inference and missing data. Biometrika 63(3), 581–592 (1976) Ibrahim et al. [2001] Ibrahim, J.G., Chen, M.-H., Lipsitz, S.R.: Missing responses in generalised linear mixed models when the missing data mechanism is nonignorable. Biometrika (2001) Mohan et al. [2018] Mohan, K., Thoemmes, F., Pearl, J.: Estimation with incomplete data: The linear case. In: IJCAI, pp. 5082–5088 (2018) Hunt and Jorgensen [2003] Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Ramoni, M., Sebastiani, P., Cohen, P.: Bayesian clustering by dynamics. Machine learning 47(1), 91–121 (2002) Xiong and Yeung [2004] Xiong, Y., Yeung, D.-Y.: Time series clustering with arma mixtures. Pattern Recognition 37(8), 1675–1689 (2004) Little and Rubin [2019] Little, R.J., Rubin, D.B.: Statistical Analysis with Missing Data, (2019) Dempster et al. [1977] Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society (1977) Rubin [1976] Rubin, D.B.: Inference and missing data. Biometrika 63(3), 581–592 (1976) Ibrahim et al. [2001] Ibrahim, J.G., Chen, M.-H., Lipsitz, S.R.: Missing responses in generalised linear mixed models when the missing data mechanism is nonignorable. Biometrika (2001) Mohan et al. [2018] Mohan, K., Thoemmes, F., Pearl, J.: Estimation with incomplete data: The linear case. In: IJCAI, pp. 5082–5088 (2018) Hunt and Jorgensen [2003] Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Xiong, Y., Yeung, D.-Y.: Time series clustering with arma mixtures. Pattern Recognition 37(8), 1675–1689 (2004) Little and Rubin [2019] Little, R.J., Rubin, D.B.: Statistical Analysis with Missing Data, (2019) Dempster et al. [1977] Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society (1977) Rubin [1976] Rubin, D.B.: Inference and missing data. Biometrika 63(3), 581–592 (1976) Ibrahim et al. [2001] Ibrahim, J.G., Chen, M.-H., Lipsitz, S.R.: Missing responses in generalised linear mixed models when the missing data mechanism is nonignorable. Biometrika (2001) Mohan et al. [2018] Mohan, K., Thoemmes, F., Pearl, J.: Estimation with incomplete data: The linear case. In: IJCAI, pp. 5082–5088 (2018) Hunt and Jorgensen [2003] Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Little, R.J., Rubin, D.B.: Statistical Analysis with Missing Data, (2019) Dempster et al. [1977] Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society (1977) Rubin [1976] Rubin, D.B.: Inference and missing data. Biometrika 63(3), 581–592 (1976) Ibrahim et al. [2001] Ibrahim, J.G., Chen, M.-H., Lipsitz, S.R.: Missing responses in generalised linear mixed models when the missing data mechanism is nonignorable. Biometrika (2001) Mohan et al. [2018] Mohan, K., Thoemmes, F., Pearl, J.: Estimation with incomplete data: The linear case. In: IJCAI, pp. 5082–5088 (2018) Hunt and Jorgensen [2003] Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society (1977) Rubin [1976] Rubin, D.B.: Inference and missing data. Biometrika 63(3), 581–592 (1976) Ibrahim et al. [2001] Ibrahim, J.G., Chen, M.-H., Lipsitz, S.R.: Missing responses in generalised linear mixed models when the missing data mechanism is nonignorable. Biometrika (2001) Mohan et al. [2018] Mohan, K., Thoemmes, F., Pearl, J.: Estimation with incomplete data: The linear case. In: IJCAI, pp. 5082–5088 (2018) Hunt and Jorgensen [2003] Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Rubin, D.B.: Inference and missing data. Biometrika 63(3), 581–592 (1976) Ibrahim et al. [2001] Ibrahim, J.G., Chen, M.-H., Lipsitz, S.R.: Missing responses in generalised linear mixed models when the missing data mechanism is nonignorable. Biometrika (2001) Mohan et al. [2018] Mohan, K., Thoemmes, F., Pearl, J.: Estimation with incomplete data: The linear case. In: IJCAI, pp. 5082–5088 (2018) Hunt and Jorgensen [2003] Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Ibrahim, J.G., Chen, M.-H., Lipsitz, S.R.: Missing responses in generalised linear mixed models when the missing data mechanism is nonignorable. Biometrika (2001) Mohan et al. [2018] Mohan, K., Thoemmes, F., Pearl, J.: Estimation with incomplete data: The linear case. In: IJCAI, pp. 5082–5088 (2018) Hunt and Jorgensen [2003] Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Mohan, K., Thoemmes, F., Pearl, J.: Estimation with incomplete data: The linear case. In: IJCAI, pp. 5082–5088 (2018) Hunt and Jorgensen [2003] Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. 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Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Bouveyron, C., Brunet-Saumard, C.: Model-based clustering of high-dimensional data: A review. Computational Statistics & Data Analysis 71, 52–78 (2014) Marbac et al. [2017] Marbac, M., Biernacki, C., Vandewalle, V.: Model-based clustering of gaussian copulas for mixed data. Communications in Statistics-Theory and Methods (2017) Ramoni et al. [2002] Ramoni, M., Sebastiani, P., Cohen, P.: Bayesian clustering by dynamics. Machine learning 47(1), 91–121 (2002) Xiong and Yeung [2004] Xiong, Y., Yeung, D.-Y.: Time series clustering with arma mixtures. Pattern Recognition 37(8), 1675–1689 (2004) Little and Rubin [2019] Little, R.J., Rubin, D.B.: Statistical Analysis with Missing Data, (2019) Dempster et al. [1977] Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society (1977) Rubin [1976] Rubin, D.B.: Inference and missing data. Biometrika 63(3), 581–592 (1976) Ibrahim et al. [2001] Ibrahim, J.G., Chen, M.-H., Lipsitz, S.R.: Missing responses in generalised linear mixed models when the missing data mechanism is nonignorable. Biometrika (2001) Mohan et al. [2018] Mohan, K., Thoemmes, F., Pearl, J.: Estimation with incomplete data: The linear case. In: IJCAI, pp. 5082–5088 (2018) Hunt and Jorgensen [2003] Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Marbac, M., Biernacki, C., Vandewalle, V.: Model-based clustering of gaussian copulas for mixed data. Communications in Statistics-Theory and Methods (2017) Ramoni et al. [2002] Ramoni, M., Sebastiani, P., Cohen, P.: Bayesian clustering by dynamics. Machine learning 47(1), 91–121 (2002) Xiong and Yeung [2004] Xiong, Y., Yeung, D.-Y.: Time series clustering with arma mixtures. Pattern Recognition 37(8), 1675–1689 (2004) Little and Rubin [2019] Little, R.J., Rubin, D.B.: Statistical Analysis with Missing Data, (2019) Dempster et al. [1977] Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society (1977) Rubin [1976] Rubin, D.B.: Inference and missing data. Biometrika 63(3), 581–592 (1976) Ibrahim et al. [2001] Ibrahim, J.G., Chen, M.-H., Lipsitz, S.R.: Missing responses in generalised linear mixed models when the missing data mechanism is nonignorable. Biometrika (2001) Mohan et al. [2018] Mohan, K., Thoemmes, F., Pearl, J.: Estimation with incomplete data: The linear case. In: IJCAI, pp. 5082–5088 (2018) Hunt and Jorgensen [2003] Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Ramoni, M., Sebastiani, P., Cohen, P.: Bayesian clustering by dynamics. Machine learning 47(1), 91–121 (2002) Xiong and Yeung [2004] Xiong, Y., Yeung, D.-Y.: Time series clustering with arma mixtures. Pattern Recognition 37(8), 1675–1689 (2004) Little and Rubin [2019] Little, R.J., Rubin, D.B.: Statistical Analysis with Missing Data, (2019) Dempster et al. [1977] Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society (1977) Rubin [1976] Rubin, D.B.: Inference and missing data. Biometrika 63(3), 581–592 (1976) Ibrahim et al. [2001] Ibrahim, J.G., Chen, M.-H., Lipsitz, S.R.: Missing responses in generalised linear mixed models when the missing data mechanism is nonignorable. Biometrika (2001) Mohan et al. [2018] Mohan, K., Thoemmes, F., Pearl, J.: Estimation with incomplete data: The linear case. In: IJCAI, pp. 5082–5088 (2018) Hunt and Jorgensen [2003] Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Xiong, Y., Yeung, D.-Y.: Time series clustering with arma mixtures. Pattern Recognition 37(8), 1675–1689 (2004) Little and Rubin [2019] Little, R.J., Rubin, D.B.: Statistical Analysis with Missing Data, (2019) Dempster et al. [1977] Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society (1977) Rubin [1976] Rubin, D.B.: Inference and missing data. Biometrika 63(3), 581–592 (1976) Ibrahim et al. [2001] Ibrahim, J.G., Chen, M.-H., Lipsitz, S.R.: Missing responses in generalised linear mixed models when the missing data mechanism is nonignorable. Biometrika (2001) Mohan et al. [2018] Mohan, K., Thoemmes, F., Pearl, J.: Estimation with incomplete data: The linear case. In: IJCAI, pp. 5082–5088 (2018) Hunt and Jorgensen [2003] Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Little, R.J., Rubin, D.B.: Statistical Analysis with Missing Data, (2019) Dempster et al. [1977] Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society (1977) Rubin [1976] Rubin, D.B.: Inference and missing data. Biometrika 63(3), 581–592 (1976) Ibrahim et al. [2001] Ibrahim, J.G., Chen, M.-H., Lipsitz, S.R.: Missing responses in generalised linear mixed models when the missing data mechanism is nonignorable. Biometrika (2001) Mohan et al. [2018] Mohan, K., Thoemmes, F., Pearl, J.: Estimation with incomplete data: The linear case. In: IJCAI, pp. 5082–5088 (2018) Hunt and Jorgensen [2003] Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society (1977) Rubin [1976] Rubin, D.B.: Inference and missing data. Biometrika 63(3), 581–592 (1976) Ibrahim et al. [2001] Ibrahim, J.G., Chen, M.-H., Lipsitz, S.R.: Missing responses in generalised linear mixed models when the missing data mechanism is nonignorable. Biometrika (2001) Mohan et al. [2018] Mohan, K., Thoemmes, F., Pearl, J.: Estimation with incomplete data: The linear case. In: IJCAI, pp. 5082–5088 (2018) Hunt and Jorgensen [2003] Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Rubin, D.B.: Inference and missing data. Biometrika 63(3), 581–592 (1976) Ibrahim et al. [2001] Ibrahim, J.G., Chen, M.-H., Lipsitz, S.R.: Missing responses in generalised linear mixed models when the missing data mechanism is nonignorable. Biometrika (2001) Mohan et al. [2018] Mohan, K., Thoemmes, F., Pearl, J.: Estimation with incomplete data: The linear case. In: IJCAI, pp. 5082–5088 (2018) Hunt and Jorgensen [2003] Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Ibrahim, J.G., Chen, M.-H., Lipsitz, S.R.: Missing responses in generalised linear mixed models when the missing data mechanism is nonignorable. Biometrika (2001) Mohan et al. [2018] Mohan, K., Thoemmes, F., Pearl, J.: Estimation with incomplete data: The linear case. In: IJCAI, pp. 5082–5088 (2018) Hunt and Jorgensen [2003] Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Mohan, K., Thoemmes, F., Pearl, J.: Estimation with incomplete data: The linear case. In: IJCAI, pp. 5082–5088 (2018) Hunt and Jorgensen [2003] Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003)
- Bouveyron, C., Brunet-Saumard, C.: Model-based clustering of high-dimensional data: A review. Computational Statistics & Data Analysis 71, 52–78 (2014) Marbac et al. [2017] Marbac, M., Biernacki, C., Vandewalle, V.: Model-based clustering of gaussian copulas for mixed data. Communications in Statistics-Theory and Methods (2017) Ramoni et al. [2002] Ramoni, M., Sebastiani, P., Cohen, P.: Bayesian clustering by dynamics. Machine learning 47(1), 91–121 (2002) Xiong and Yeung [2004] Xiong, Y., Yeung, D.-Y.: Time series clustering with arma mixtures. Pattern Recognition 37(8), 1675–1689 (2004) Little and Rubin [2019] Little, R.J., Rubin, D.B.: Statistical Analysis with Missing Data, (2019) Dempster et al. [1977] Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society (1977) Rubin [1976] Rubin, D.B.: Inference and missing data. Biometrika 63(3), 581–592 (1976) Ibrahim et al. [2001] Ibrahim, J.G., Chen, M.-H., Lipsitz, S.R.: Missing responses in generalised linear mixed models when the missing data mechanism is nonignorable. Biometrika (2001) Mohan et al. [2018] Mohan, K., Thoemmes, F., Pearl, J.: Estimation with incomplete data: The linear case. In: IJCAI, pp. 5082–5088 (2018) Hunt and Jorgensen [2003] Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Marbac, M., Biernacki, C., Vandewalle, V.: Model-based clustering of gaussian copulas for mixed data. Communications in Statistics-Theory and Methods (2017) Ramoni et al. [2002] Ramoni, M., Sebastiani, P., Cohen, P.: Bayesian clustering by dynamics. Machine learning 47(1), 91–121 (2002) Xiong and Yeung [2004] Xiong, Y., Yeung, D.-Y.: Time series clustering with arma mixtures. Pattern Recognition 37(8), 1675–1689 (2004) Little and Rubin [2019] Little, R.J., Rubin, D.B.: Statistical Analysis with Missing Data, (2019) Dempster et al. [1977] Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society (1977) Rubin [1976] Rubin, D.B.: Inference and missing data. Biometrika 63(3), 581–592 (1976) Ibrahim et al. [2001] Ibrahim, J.G., Chen, M.-H., Lipsitz, S.R.: Missing responses in generalised linear mixed models when the missing data mechanism is nonignorable. Biometrika (2001) Mohan et al. [2018] Mohan, K., Thoemmes, F., Pearl, J.: Estimation with incomplete data: The linear case. In: IJCAI, pp. 5082–5088 (2018) Hunt and Jorgensen [2003] Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Ramoni, M., Sebastiani, P., Cohen, P.: Bayesian clustering by dynamics. Machine learning 47(1), 91–121 (2002) Xiong and Yeung [2004] Xiong, Y., Yeung, D.-Y.: Time series clustering with arma mixtures. Pattern Recognition 37(8), 1675–1689 (2004) Little and Rubin [2019] Little, R.J., Rubin, D.B.: Statistical Analysis with Missing Data, (2019) Dempster et al. [1977] Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society (1977) Rubin [1976] Rubin, D.B.: Inference and missing data. Biometrika 63(3), 581–592 (1976) Ibrahim et al. [2001] Ibrahim, J.G., Chen, M.-H., Lipsitz, S.R.: Missing responses in generalised linear mixed models when the missing data mechanism is nonignorable. Biometrika (2001) Mohan et al. [2018] Mohan, K., Thoemmes, F., Pearl, J.: Estimation with incomplete data: The linear case. In: IJCAI, pp. 5082–5088 (2018) Hunt and Jorgensen [2003] Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Xiong, Y., Yeung, D.-Y.: Time series clustering with arma mixtures. Pattern Recognition 37(8), 1675–1689 (2004) Little and Rubin [2019] Little, R.J., Rubin, D.B.: Statistical Analysis with Missing Data, (2019) Dempster et al. [1977] Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society (1977) Rubin [1976] Rubin, D.B.: Inference and missing data. Biometrika 63(3), 581–592 (1976) Ibrahim et al. [2001] Ibrahim, J.G., Chen, M.-H., Lipsitz, S.R.: Missing responses in generalised linear mixed models when the missing data mechanism is nonignorable. Biometrika (2001) Mohan et al. [2018] Mohan, K., Thoemmes, F., Pearl, J.: Estimation with incomplete data: The linear case. In: IJCAI, pp. 5082–5088 (2018) Hunt and Jorgensen [2003] Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Little, R.J., Rubin, D.B.: Statistical Analysis with Missing Data, (2019) Dempster et al. [1977] Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society (1977) Rubin [1976] Rubin, D.B.: Inference and missing data. Biometrika 63(3), 581–592 (1976) Ibrahim et al. [2001] Ibrahim, J.G., Chen, M.-H., Lipsitz, S.R.: Missing responses in generalised linear mixed models when the missing data mechanism is nonignorable. Biometrika (2001) Mohan et al. [2018] Mohan, K., Thoemmes, F., Pearl, J.: Estimation with incomplete data: The linear case. In: IJCAI, pp. 5082–5088 (2018) Hunt and Jorgensen [2003] Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society (1977) Rubin [1976] Rubin, D.B.: Inference and missing data. Biometrika 63(3), 581–592 (1976) Ibrahim et al. [2001] Ibrahim, J.G., Chen, M.-H., Lipsitz, S.R.: Missing responses in generalised linear mixed models when the missing data mechanism is nonignorable. Biometrika (2001) Mohan et al. [2018] Mohan, K., Thoemmes, F., Pearl, J.: Estimation with incomplete data: The linear case. In: IJCAI, pp. 5082–5088 (2018) Hunt and Jorgensen [2003] Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Rubin, D.B.: Inference and missing data. Biometrika 63(3), 581–592 (1976) Ibrahim et al. [2001] Ibrahim, J.G., Chen, M.-H., Lipsitz, S.R.: Missing responses in generalised linear mixed models when the missing data mechanism is nonignorable. Biometrika (2001) Mohan et al. [2018] Mohan, K., Thoemmes, F., Pearl, J.: Estimation with incomplete data: The linear case. In: IJCAI, pp. 5082–5088 (2018) Hunt and Jorgensen [2003] Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Ibrahim, J.G., Chen, M.-H., Lipsitz, S.R.: Missing responses in generalised linear mixed models when the missing data mechanism is nonignorable. Biometrika (2001) Mohan et al. [2018] Mohan, K., Thoemmes, F., Pearl, J.: Estimation with incomplete data: The linear case. In: IJCAI, pp. 5082–5088 (2018) Hunt and Jorgensen [2003] Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Mohan, K., Thoemmes, F., Pearl, J.: Estimation with incomplete data: The linear case. In: IJCAI, pp. 5082–5088 (2018) Hunt and Jorgensen [2003] Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003)
- Marbac, M., Biernacki, C., Vandewalle, V.: Model-based clustering of gaussian copulas for mixed data. Communications in Statistics-Theory and Methods (2017) Ramoni et al. [2002] Ramoni, M., Sebastiani, P., Cohen, P.: Bayesian clustering by dynamics. Machine learning 47(1), 91–121 (2002) Xiong and Yeung [2004] Xiong, Y., Yeung, D.-Y.: Time series clustering with arma mixtures. Pattern Recognition 37(8), 1675–1689 (2004) Little and Rubin [2019] Little, R.J., Rubin, D.B.: Statistical Analysis with Missing Data, (2019) Dempster et al. [1977] Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society (1977) Rubin [1976] Rubin, D.B.: Inference and missing data. Biometrika 63(3), 581–592 (1976) Ibrahim et al. [2001] Ibrahim, J.G., Chen, M.-H., Lipsitz, S.R.: Missing responses in generalised linear mixed models when the missing data mechanism is nonignorable. Biometrika (2001) Mohan et al. [2018] Mohan, K., Thoemmes, F., Pearl, J.: Estimation with incomplete data: The linear case. In: IJCAI, pp. 5082–5088 (2018) Hunt and Jorgensen [2003] Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Ramoni, M., Sebastiani, P., Cohen, P.: Bayesian clustering by dynamics. Machine learning 47(1), 91–121 (2002) Xiong and Yeung [2004] Xiong, Y., Yeung, D.-Y.: Time series clustering with arma mixtures. Pattern Recognition 37(8), 1675–1689 (2004) Little and Rubin [2019] Little, R.J., Rubin, D.B.: Statistical Analysis with Missing Data, (2019) Dempster et al. [1977] Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society (1977) Rubin [1976] Rubin, D.B.: Inference and missing data. Biometrika 63(3), 581–592 (1976) Ibrahim et al. [2001] Ibrahim, J.G., Chen, M.-H., Lipsitz, S.R.: Missing responses in generalised linear mixed models when the missing data mechanism is nonignorable. Biometrika (2001) Mohan et al. [2018] Mohan, K., Thoemmes, F., Pearl, J.: Estimation with incomplete data: The linear case. In: IJCAI, pp. 5082–5088 (2018) Hunt and Jorgensen [2003] Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Xiong, Y., Yeung, D.-Y.: Time series clustering with arma mixtures. Pattern Recognition 37(8), 1675–1689 (2004) Little and Rubin [2019] Little, R.J., Rubin, D.B.: Statistical Analysis with Missing Data, (2019) Dempster et al. [1977] Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society (1977) Rubin [1976] Rubin, D.B.: Inference and missing data. Biometrika 63(3), 581–592 (1976) Ibrahim et al. [2001] Ibrahim, J.G., Chen, M.-H., Lipsitz, S.R.: Missing responses in generalised linear mixed models when the missing data mechanism is nonignorable. Biometrika (2001) Mohan et al. [2018] Mohan, K., Thoemmes, F., Pearl, J.: Estimation with incomplete data: The linear case. In: IJCAI, pp. 5082–5088 (2018) Hunt and Jorgensen [2003] Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Little, R.J., Rubin, D.B.: Statistical Analysis with Missing Data, (2019) Dempster et al. [1977] Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society (1977) Rubin [1976] Rubin, D.B.: Inference and missing data. Biometrika 63(3), 581–592 (1976) Ibrahim et al. [2001] Ibrahim, J.G., Chen, M.-H., Lipsitz, S.R.: Missing responses in generalised linear mixed models when the missing data mechanism is nonignorable. Biometrika (2001) Mohan et al. [2018] Mohan, K., Thoemmes, F., Pearl, J.: Estimation with incomplete data: The linear case. In: IJCAI, pp. 5082–5088 (2018) Hunt and Jorgensen [2003] Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society (1977) Rubin [1976] Rubin, D.B.: Inference and missing data. Biometrika 63(3), 581–592 (1976) Ibrahim et al. [2001] Ibrahim, J.G., Chen, M.-H., Lipsitz, S.R.: Missing responses in generalised linear mixed models when the missing data mechanism is nonignorable. Biometrika (2001) Mohan et al. [2018] Mohan, K., Thoemmes, F., Pearl, J.: Estimation with incomplete data: The linear case. In: IJCAI, pp. 5082–5088 (2018) Hunt and Jorgensen [2003] Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Rubin, D.B.: Inference and missing data. Biometrika 63(3), 581–592 (1976) Ibrahim et al. [2001] Ibrahim, J.G., Chen, M.-H., Lipsitz, S.R.: Missing responses in generalised linear mixed models when the missing data mechanism is nonignorable. Biometrika (2001) Mohan et al. [2018] Mohan, K., Thoemmes, F., Pearl, J.: Estimation with incomplete data: The linear case. In: IJCAI, pp. 5082–5088 (2018) Hunt and Jorgensen [2003] Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Ibrahim, J.G., Chen, M.-H., Lipsitz, S.R.: Missing responses in generalised linear mixed models when the missing data mechanism is nonignorable. Biometrika (2001) Mohan et al. [2018] Mohan, K., Thoemmes, F., Pearl, J.: Estimation with incomplete data: The linear case. In: IJCAI, pp. 5082–5088 (2018) Hunt and Jorgensen [2003] Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Mohan, K., Thoemmes, F., Pearl, J.: Estimation with incomplete data: The linear case. In: IJCAI, pp. 5082–5088 (2018) Hunt and Jorgensen [2003] Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003)
- Ramoni, M., Sebastiani, P., Cohen, P.: Bayesian clustering by dynamics. Machine learning 47(1), 91–121 (2002) Xiong and Yeung [2004] Xiong, Y., Yeung, D.-Y.: Time series clustering with arma mixtures. Pattern Recognition 37(8), 1675–1689 (2004) Little and Rubin [2019] Little, R.J., Rubin, D.B.: Statistical Analysis with Missing Data, (2019) Dempster et al. [1977] Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society (1977) Rubin [1976] Rubin, D.B.: Inference and missing data. Biometrika 63(3), 581–592 (1976) Ibrahim et al. [2001] Ibrahim, J.G., Chen, M.-H., Lipsitz, S.R.: Missing responses in generalised linear mixed models when the missing data mechanism is nonignorable. Biometrika (2001) Mohan et al. [2018] Mohan, K., Thoemmes, F., Pearl, J.: Estimation with incomplete data: The linear case. In: IJCAI, pp. 5082–5088 (2018) Hunt and Jorgensen [2003] Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Xiong, Y., Yeung, D.-Y.: Time series clustering with arma mixtures. Pattern Recognition 37(8), 1675–1689 (2004) Little and Rubin [2019] Little, R.J., Rubin, D.B.: Statistical Analysis with Missing Data, (2019) Dempster et al. [1977] Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society (1977) Rubin [1976] Rubin, D.B.: Inference and missing data. Biometrika 63(3), 581–592 (1976) Ibrahim et al. [2001] Ibrahim, J.G., Chen, M.-H., Lipsitz, S.R.: Missing responses in generalised linear mixed models when the missing data mechanism is nonignorable. Biometrika (2001) Mohan et al. [2018] Mohan, K., Thoemmes, F., Pearl, J.: Estimation with incomplete data: The linear case. In: IJCAI, pp. 5082–5088 (2018) Hunt and Jorgensen [2003] Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Little, R.J., Rubin, D.B.: Statistical Analysis with Missing Data, (2019) Dempster et al. [1977] Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society (1977) Rubin [1976] Rubin, D.B.: Inference and missing data. Biometrika 63(3), 581–592 (1976) Ibrahim et al. [2001] Ibrahim, J.G., Chen, M.-H., Lipsitz, S.R.: Missing responses in generalised linear mixed models when the missing data mechanism is nonignorable. Biometrika (2001) Mohan et al. [2018] Mohan, K., Thoemmes, F., Pearl, J.: Estimation with incomplete data: The linear case. In: IJCAI, pp. 5082–5088 (2018) Hunt and Jorgensen [2003] Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society (1977) Rubin [1976] Rubin, D.B.: Inference and missing data. Biometrika 63(3), 581–592 (1976) Ibrahim et al. [2001] Ibrahim, J.G., Chen, M.-H., Lipsitz, S.R.: Missing responses in generalised linear mixed models when the missing data mechanism is nonignorable. Biometrika (2001) Mohan et al. [2018] Mohan, K., Thoemmes, F., Pearl, J.: Estimation with incomplete data: The linear case. In: IJCAI, pp. 5082–5088 (2018) Hunt and Jorgensen [2003] Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Rubin, D.B.: Inference and missing data. Biometrika 63(3), 581–592 (1976) Ibrahim et al. [2001] Ibrahim, J.G., Chen, M.-H., Lipsitz, S.R.: Missing responses in generalised linear mixed models when the missing data mechanism is nonignorable. Biometrika (2001) Mohan et al. [2018] Mohan, K., Thoemmes, F., Pearl, J.: Estimation with incomplete data: The linear case. In: IJCAI, pp. 5082–5088 (2018) Hunt and Jorgensen [2003] Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Ibrahim, J.G., Chen, M.-H., Lipsitz, S.R.: Missing responses in generalised linear mixed models when the missing data mechanism is nonignorable. Biometrika (2001) Mohan et al. [2018] Mohan, K., Thoemmes, F., Pearl, J.: Estimation with incomplete data: The linear case. In: IJCAI, pp. 5082–5088 (2018) Hunt and Jorgensen [2003] Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Mohan, K., Thoemmes, F., Pearl, J.: Estimation with incomplete data: The linear case. In: IJCAI, pp. 5082–5088 (2018) Hunt and Jorgensen [2003] Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003)
- Xiong, Y., Yeung, D.-Y.: Time series clustering with arma mixtures. Pattern Recognition 37(8), 1675–1689 (2004) Little and Rubin [2019] Little, R.J., Rubin, D.B.: Statistical Analysis with Missing Data, (2019) Dempster et al. [1977] Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society (1977) Rubin [1976] Rubin, D.B.: Inference and missing data. Biometrika 63(3), 581–592 (1976) Ibrahim et al. [2001] Ibrahim, J.G., Chen, M.-H., Lipsitz, S.R.: Missing responses in generalised linear mixed models when the missing data mechanism is nonignorable. Biometrika (2001) Mohan et al. [2018] Mohan, K., Thoemmes, F., Pearl, J.: Estimation with incomplete data: The linear case. In: IJCAI, pp. 5082–5088 (2018) Hunt and Jorgensen [2003] Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Little, R.J., Rubin, D.B.: Statistical Analysis with Missing Data, (2019) Dempster et al. [1977] Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society (1977) Rubin [1976] Rubin, D.B.: Inference and missing data. Biometrika 63(3), 581–592 (1976) Ibrahim et al. [2001] Ibrahim, J.G., Chen, M.-H., Lipsitz, S.R.: Missing responses in generalised linear mixed models when the missing data mechanism is nonignorable. Biometrika (2001) Mohan et al. [2018] Mohan, K., Thoemmes, F., Pearl, J.: Estimation with incomplete data: The linear case. In: IJCAI, pp. 5082–5088 (2018) Hunt and Jorgensen [2003] Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society (1977) Rubin [1976] Rubin, D.B.: Inference and missing data. Biometrika 63(3), 581–592 (1976) Ibrahim et al. [2001] Ibrahim, J.G., Chen, M.-H., Lipsitz, S.R.: Missing responses in generalised linear mixed models when the missing data mechanism is nonignorable. Biometrika (2001) Mohan et al. [2018] Mohan, K., Thoemmes, F., Pearl, J.: Estimation with incomplete data: The linear case. In: IJCAI, pp. 5082–5088 (2018) Hunt and Jorgensen [2003] Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Rubin, D.B.: Inference and missing data. Biometrika 63(3), 581–592 (1976) Ibrahim et al. [2001] Ibrahim, J.G., Chen, M.-H., Lipsitz, S.R.: Missing responses in generalised linear mixed models when the missing data mechanism is nonignorable. Biometrika (2001) Mohan et al. [2018] Mohan, K., Thoemmes, F., Pearl, J.: Estimation with incomplete data: The linear case. In: IJCAI, pp. 5082–5088 (2018) Hunt and Jorgensen [2003] Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Ibrahim, J.G., Chen, M.-H., Lipsitz, S.R.: Missing responses in generalised linear mixed models when the missing data mechanism is nonignorable. Biometrika (2001) Mohan et al. [2018] Mohan, K., Thoemmes, F., Pearl, J.: Estimation with incomplete data: The linear case. In: IJCAI, pp. 5082–5088 (2018) Hunt and Jorgensen [2003] Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Mohan, K., Thoemmes, F., Pearl, J.: Estimation with incomplete data: The linear case. In: IJCAI, pp. 5082–5088 (2018) Hunt and Jorgensen [2003] Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003)
- Little, R.J., Rubin, D.B.: Statistical Analysis with Missing Data, (2019) Dempster et al. [1977] Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society (1977) Rubin [1976] Rubin, D.B.: Inference and missing data. Biometrika 63(3), 581–592 (1976) Ibrahim et al. [2001] Ibrahim, J.G., Chen, M.-H., Lipsitz, S.R.: Missing responses in generalised linear mixed models when the missing data mechanism is nonignorable. Biometrika (2001) Mohan et al. [2018] Mohan, K., Thoemmes, F., Pearl, J.: Estimation with incomplete data: The linear case. In: IJCAI, pp. 5082–5088 (2018) Hunt and Jorgensen [2003] Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society (1977) Rubin [1976] Rubin, D.B.: Inference and missing data. Biometrika 63(3), 581–592 (1976) Ibrahim et al. [2001] Ibrahim, J.G., Chen, M.-H., Lipsitz, S.R.: Missing responses in generalised linear mixed models when the missing data mechanism is nonignorable. Biometrika (2001) Mohan et al. [2018] Mohan, K., Thoemmes, F., Pearl, J.: Estimation with incomplete data: The linear case. In: IJCAI, pp. 5082–5088 (2018) Hunt and Jorgensen [2003] Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Rubin, D.B.: Inference and missing data. Biometrika 63(3), 581–592 (1976) Ibrahim et al. [2001] Ibrahim, J.G., Chen, M.-H., Lipsitz, S.R.: Missing responses in generalised linear mixed models when the missing data mechanism is nonignorable. Biometrika (2001) Mohan et al. [2018] Mohan, K., Thoemmes, F., Pearl, J.: Estimation with incomplete data: The linear case. In: IJCAI, pp. 5082–5088 (2018) Hunt and Jorgensen [2003] Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Ibrahim, J.G., Chen, M.-H., Lipsitz, S.R.: Missing responses in generalised linear mixed models when the missing data mechanism is nonignorable. Biometrika (2001) Mohan et al. [2018] Mohan, K., Thoemmes, F., Pearl, J.: Estimation with incomplete data: The linear case. In: IJCAI, pp. 5082–5088 (2018) Hunt and Jorgensen [2003] Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Mohan, K., Thoemmes, F., Pearl, J.: Estimation with incomplete data: The linear case. In: IJCAI, pp. 5082–5088 (2018) Hunt and Jorgensen [2003] Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. 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[2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. 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[2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003)
- Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society (1977) Rubin [1976] Rubin, D.B.: Inference and missing data. Biometrika 63(3), 581–592 (1976) Ibrahim et al. [2001] Ibrahim, J.G., Chen, M.-H., Lipsitz, S.R.: Missing responses in generalised linear mixed models when the missing data mechanism is nonignorable. Biometrika (2001) Mohan et al. [2018] Mohan, K., Thoemmes, F., Pearl, J.: Estimation with incomplete data: The linear case. In: IJCAI, pp. 5082–5088 (2018) Hunt and Jorgensen [2003] Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Rubin, D.B.: Inference and missing data. Biometrika 63(3), 581–592 (1976) Ibrahim et al. [2001] Ibrahim, J.G., Chen, M.-H., Lipsitz, S.R.: Missing responses in generalised linear mixed models when the missing data mechanism is nonignorable. Biometrika (2001) Mohan et al. [2018] Mohan, K., Thoemmes, F., Pearl, J.: Estimation with incomplete data: The linear case. In: IJCAI, pp. 5082–5088 (2018) Hunt and Jorgensen [2003] Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Ibrahim, J.G., Chen, M.-H., Lipsitz, S.R.: Missing responses in generalised linear mixed models when the missing data mechanism is nonignorable. Biometrika (2001) Mohan et al. [2018] Mohan, K., Thoemmes, F., Pearl, J.: Estimation with incomplete data: The linear case. In: IJCAI, pp. 5082–5088 (2018) Hunt and Jorgensen [2003] Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Mohan, K., Thoemmes, F., Pearl, J.: Estimation with incomplete data: The linear case. In: IJCAI, pp. 5082–5088 (2018) Hunt and Jorgensen [2003] Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003)
- Rubin, D.B.: Inference and missing data. Biometrika 63(3), 581–592 (1976) Ibrahim et al. [2001] Ibrahim, J.G., Chen, M.-H., Lipsitz, S.R.: Missing responses in generalised linear mixed models when the missing data mechanism is nonignorable. Biometrika (2001) Mohan et al. [2018] Mohan, K., Thoemmes, F., Pearl, J.: Estimation with incomplete data: The linear case. In: IJCAI, pp. 5082–5088 (2018) Hunt and Jorgensen [2003] Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Ibrahim, J.G., Chen, M.-H., Lipsitz, S.R.: Missing responses in generalised linear mixed models when the missing data mechanism is nonignorable. Biometrika (2001) Mohan et al. [2018] Mohan, K., Thoemmes, F., Pearl, J.: Estimation with incomplete data: The linear case. In: IJCAI, pp. 5082–5088 (2018) Hunt and Jorgensen [2003] Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Mohan, K., Thoemmes, F., Pearl, J.: Estimation with incomplete data: The linear case. In: IJCAI, pp. 5082–5088 (2018) Hunt and Jorgensen [2003] Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. 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Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003)
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[2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Mohan, K., Thoemmes, F., Pearl, J.: Estimation with incomplete data: The linear case. In: IJCAI, pp. 5082–5088 (2018) Hunt and Jorgensen [2003] Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. 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JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. 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[2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003)
- Mohan, K., Thoemmes, F., Pearl, J.: Estimation with incomplete data: The linear case. In: IJCAI, pp. 5082–5088 (2018) Hunt and Jorgensen [2003] Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003)
- Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis 41, 429–440 (2003) Serafini et al. [2020] Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. 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[2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. 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[2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003)
- Serafini, A., Murphy, T.B., Scrucca, L.: Handling missing data in model-based clustering. arXiv preprint (2020) Chi et al. [2016] Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003)
- Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-pod: A method for k-means clustering of missing data. The American Statistician 70(1), 91–99 (2016) Du Roy De Chaumaray and Marbac [2020] Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003)
- Du Roy De Chaumaray, M., Marbac, M.: Clustering data with nonignorable missingness using semi-parametric mixture models. arXiv preprint (2020) Beunckens et al. [2008] Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003)
- Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C.: A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 64(1), 96–105 (2008) Kuha et al. [2018] Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003)
- Kuha, J., Katsikatsou, M., Moustaki, I.: Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society (2018) Josse et al. [2019] Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Josse, J., Prost, N., Scornet, E., Varoquaux, G.: On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931 (2019) Banfield and Raftery [1993] Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics, 803–821 (1993) Geweke et al. [1994] Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. 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Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003)
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Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Geweke, J., Keane, M., Runkle, D.: Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics (1994) McParland and Gormley [2016] McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. 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[2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. 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Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003)
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Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. 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Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. 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Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. 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Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003)
- McParland, D., Gormley, I.C.: Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification 10(2), 155–169 (2016) Heckman [1979] Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003)
- Heckman, J.J.: Sample selection bias as a specification error. Econometrica (1979) Little [1993] Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003)
- Little, R.J.: Pattern-mixture models for multivariate incomplete data. JASA (1993) du Roy de Chaumaray and Marbac [2023] Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003)
- Chaumaray, M., Marbac, M.: Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification, 1–42 (2023) Sportisse et al. [2020] Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Sportisse, A., Boyer, C., Josse, J.: Imputation and low-rank estimation with missing not at random data. Statistics and Computing 30(6), 1629–1643 (2020) Mohan [2018] Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003)
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Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003)
- Mohan, K.: On handling self-masking and other hard missing data problems (2018) Molenberghs et al. [2008] Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Molenberghs, G., Beunckens, C., Sotto, C., Kenward, M.G.: Every missingness not at random model has a missingness at random counterpart with equal fit. Journal of the Royal Statistical Society B 70, 371–388 (2008) Schwarz [1978] Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003)
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Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978) Biernacki et al. [2000] Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. 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(2003) Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003)
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In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003)
- Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000) Baudry et al. [2015] Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003)
- Baudry, J.-P., et al.: Estimation and model selection for model-based clustering with the conditional classification likelihood. Electronic journal of statistics 9(1), 1041–1077 (2015) Hubert and Arabie [1985] Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003)
- Hubert, L., Arabie, P.: Comparing partitions. Journal of classification (1985) Buuren and Groothuis-Oudshoorn [2010] Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Buuren, S.v., Groothuis-Oudshoorn, K.: mice: Multivariate imputation by chained equations in r. Journal of statistical software, 1–68 (2010) Biernacki et al. [2015] Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Biernacki, C., Deregnaucourt, T., Kubicki, V.: Model-based clustering with mixed/missing data using the new software mixtcomp. In: CMStatistics 2015 (ERCIM 2015) (2015) Lê et al. [2008] Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Lê, S., Josse, J., Husson, F.: FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25(1), 1–18 (2008) Anderson [2003] Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003) Anderson, T.W.: An Introduction to Multivariate Statistical sAnalysis. Wiley, ??? (2003)
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