2000 character limit reached
Applying Non-negative Matrix Factorization with Covariates to the Longitudinal Data as Growth Curve Model (2403.05359v3)
Published 8 Mar 2024 in stat.ME
Abstract: Using Non-negative Matrix Factorization (NMF), the observed matrix can be approximated by the product of the basis and coefficient matrices. Moreover, if the coefficient vectors are explained by the covariates for each individual, the coefficient matrix can be written as the product of the parameter matrix and the covariate matrix, and additionally described in the framework of Non-negative Matrix tri-Factorization (tri-NMF) with covariates. Consequently, this is equal to the mean structure of the Growth Curve Model (GCM). The difference is that the basis matrix for GCM is given by the analyst, whereas that for NMF with covariates is unknown and optimized. In this study, we applied NMF with covariance to longitudinal data and compared it with GCM. We have also published an R package that implements this method, and we show how to use it through examples of data analyses including longitudinal measurement, spatiotemporal data and text data. In particular, we demonstrate the usefulness of Gaussian kernel functions as covariates.
- Berry MW, Gillis N, Glineur F (2009) Document classification using nonnegative matrix factorization and underapproximation. In: 2009 IEEE International Symposium on Circuits and Systems, IEEE, pp 2782–2785 Chen et al [2022] Chen WS, Ge X, Pan B (2022) A novel general kernel-based non-negative matrix factorisation approach for face recognition. Connection Science 34(1):785–810 Cichocki and Amari [2010] Cichocki A, Amari Si (2010) Families of alpha-beta-and gamma-divergences: Flexible and robust measures of similarities. Entropy 12(6):1532–1568 Čopar et al [2017] Čopar A, Žitnik M, Zupan B (2017) Scalable non-negative matrix tri-factorization. BioData mining 10:1–16 Ding et al [2006] Ding C, Li T, Peng W, et al (2006) Orthogonal nonnegative matrix t-factorizations for clustering. In: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 126–135 Févotte et al [2009] Févotte C, Bertin N, Durrieu JL (2009) Nonnegative matrix factorization with the itakura-saito divergence: With application to music analysis. Neural computation 21(3):793–830 Gaujoux and Seoighe [2010] Gaujoux R, Seoighe C (2010) A flexible r package for nonnegative matrix factorization. BMC bioinformatics 11(1):1–9 Gillis [2020] Gillis N (2020) Nonnegative matrix factorization. SIAM Hastie and Tibshirani [1993] Hastie T, Tibshirani R (1993) Varying-coefficient models. Journal of the Royal Statistical Society Series B: Statistical Methodology 55(4):757–779 Hastie et al [2009] Hastie T, Tibshirani R, Friedman JH, et al (2009) The elements of statistical learning: data mining, inference, and prediction, vol 2. Springer Hayashi et al [2016] Hayashi A, Kameoka H, Matsubayashi T, et al (2016) Non-negative periodic component analysis for music source separation. In: 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), IEEE, pp 1–9 Ito et al [2016] Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Chen WS, Ge X, Pan B (2022) A novel general kernel-based non-negative matrix factorisation approach for face recognition. Connection Science 34(1):785–810 Cichocki and Amari [2010] Cichocki A, Amari Si (2010) Families of alpha-beta-and gamma-divergences: Flexible and robust measures of similarities. Entropy 12(6):1532–1568 Čopar et al [2017] Čopar A, Žitnik M, Zupan B (2017) Scalable non-negative matrix tri-factorization. BioData mining 10:1–16 Ding et al [2006] Ding C, Li T, Peng W, et al (2006) Orthogonal nonnegative matrix t-factorizations for clustering. In: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 126–135 Févotte et al [2009] Févotte C, Bertin N, Durrieu JL (2009) Nonnegative matrix factorization with the itakura-saito divergence: With application to music analysis. Neural computation 21(3):793–830 Gaujoux and Seoighe [2010] Gaujoux R, Seoighe C (2010) A flexible r package for nonnegative matrix factorization. BMC bioinformatics 11(1):1–9 Gillis [2020] Gillis N (2020) Nonnegative matrix factorization. SIAM Hastie and Tibshirani [1993] Hastie T, Tibshirani R (1993) Varying-coefficient models. Journal of the Royal Statistical Society Series B: Statistical Methodology 55(4):757–779 Hastie et al [2009] Hastie T, Tibshirani R, Friedman JH, et al (2009) The elements of statistical learning: data mining, inference, and prediction, vol 2. Springer Hayashi et al [2016] Hayashi A, Kameoka H, Matsubayashi T, et al (2016) Non-negative periodic component analysis for music source separation. In: 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), IEEE, pp 1–9 Ito et al [2016] Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Cichocki A, Amari Si (2010) Families of alpha-beta-and gamma-divergences: Flexible and robust measures of similarities. Entropy 12(6):1532–1568 Čopar et al [2017] Čopar A, Žitnik M, Zupan B (2017) Scalable non-negative matrix tri-factorization. BioData mining 10:1–16 Ding et al [2006] Ding C, Li T, Peng W, et al (2006) Orthogonal nonnegative matrix t-factorizations for clustering. In: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 126–135 Févotte et al [2009] Févotte C, Bertin N, Durrieu JL (2009) Nonnegative matrix factorization with the itakura-saito divergence: With application to music analysis. Neural computation 21(3):793–830 Gaujoux and Seoighe [2010] Gaujoux R, Seoighe C (2010) A flexible r package for nonnegative matrix factorization. BMC bioinformatics 11(1):1–9 Gillis [2020] Gillis N (2020) Nonnegative matrix factorization. SIAM Hastie and Tibshirani [1993] Hastie T, Tibshirani R (1993) Varying-coefficient models. Journal of the Royal Statistical Society Series B: Statistical Methodology 55(4):757–779 Hastie et al [2009] Hastie T, Tibshirani R, Friedman JH, et al (2009) The elements of statistical learning: data mining, inference, and prediction, vol 2. Springer Hayashi et al [2016] Hayashi A, Kameoka H, Matsubayashi T, et al (2016) Non-negative periodic component analysis for music source separation. In: 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), IEEE, pp 1–9 Ito et al [2016] Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Čopar A, Žitnik M, Zupan B (2017) Scalable non-negative matrix tri-factorization. BioData mining 10:1–16 Ding et al [2006] Ding C, Li T, Peng W, et al (2006) Orthogonal nonnegative matrix t-factorizations for clustering. In: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 126–135 Févotte et al [2009] Févotte C, Bertin N, Durrieu JL (2009) Nonnegative matrix factorization with the itakura-saito divergence: With application to music analysis. Neural computation 21(3):793–830 Gaujoux and Seoighe [2010] Gaujoux R, Seoighe C (2010) A flexible r package for nonnegative matrix factorization. BMC bioinformatics 11(1):1–9 Gillis [2020] Gillis N (2020) Nonnegative matrix factorization. SIAM Hastie and Tibshirani [1993] Hastie T, Tibshirani R (1993) Varying-coefficient models. Journal of the Royal Statistical Society Series B: Statistical Methodology 55(4):757–779 Hastie et al [2009] Hastie T, Tibshirani R, Friedman JH, et al (2009) The elements of statistical learning: data mining, inference, and prediction, vol 2. Springer Hayashi et al [2016] Hayashi A, Kameoka H, Matsubayashi T, et al (2016) Non-negative periodic component analysis for music source separation. In: 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), IEEE, pp 1–9 Ito et al [2016] Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Ding C, Li T, Peng W, et al (2006) Orthogonal nonnegative matrix t-factorizations for clustering. In: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 126–135 Févotte et al [2009] Févotte C, Bertin N, Durrieu JL (2009) Nonnegative matrix factorization with the itakura-saito divergence: With application to music analysis. Neural computation 21(3):793–830 Gaujoux and Seoighe [2010] Gaujoux R, Seoighe C (2010) A flexible r package for nonnegative matrix factorization. BMC bioinformatics 11(1):1–9 Gillis [2020] Gillis N (2020) Nonnegative matrix factorization. SIAM Hastie and Tibshirani [1993] Hastie T, Tibshirani R (1993) Varying-coefficient models. Journal of the Royal Statistical Society Series B: Statistical Methodology 55(4):757–779 Hastie et al [2009] Hastie T, Tibshirani R, Friedman JH, et al (2009) The elements of statistical learning: data mining, inference, and prediction, vol 2. Springer Hayashi et al [2016] Hayashi A, Kameoka H, Matsubayashi T, et al (2016) Non-negative periodic component analysis for music source separation. In: 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), IEEE, pp 1–9 Ito et al [2016] Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Févotte C, Bertin N, Durrieu JL (2009) Nonnegative matrix factorization with the itakura-saito divergence: With application to music analysis. Neural computation 21(3):793–830 Gaujoux and Seoighe [2010] Gaujoux R, Seoighe C (2010) A flexible r package for nonnegative matrix factorization. BMC bioinformatics 11(1):1–9 Gillis [2020] Gillis N (2020) Nonnegative matrix factorization. SIAM Hastie and Tibshirani [1993] Hastie T, Tibshirani R (1993) Varying-coefficient models. Journal of the Royal Statistical Society Series B: Statistical Methodology 55(4):757–779 Hastie et al [2009] Hastie T, Tibshirani R, Friedman JH, et al (2009) The elements of statistical learning: data mining, inference, and prediction, vol 2. Springer Hayashi et al [2016] Hayashi A, Kameoka H, Matsubayashi T, et al (2016) Non-negative periodic component analysis for music source separation. In: 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), IEEE, pp 1–9 Ito et al [2016] Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Gaujoux R, Seoighe C (2010) A flexible r package for nonnegative matrix factorization. BMC bioinformatics 11(1):1–9 Gillis [2020] Gillis N (2020) Nonnegative matrix factorization. SIAM Hastie and Tibshirani [1993] Hastie T, Tibshirani R (1993) Varying-coefficient models. Journal of the Royal Statistical Society Series B: Statistical Methodology 55(4):757–779 Hastie et al [2009] Hastie T, Tibshirani R, Friedman JH, et al (2009) The elements of statistical learning: data mining, inference, and prediction, vol 2. Springer Hayashi et al [2016] Hayashi A, Kameoka H, Matsubayashi T, et al (2016) Non-negative periodic component analysis for music source separation. In: 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), IEEE, pp 1–9 Ito et al [2016] Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Gillis N (2020) Nonnegative matrix factorization. SIAM Hastie and Tibshirani [1993] Hastie T, Tibshirani R (1993) Varying-coefficient models. Journal of the Royal Statistical Society Series B: Statistical Methodology 55(4):757–779 Hastie et al [2009] Hastie T, Tibshirani R, Friedman JH, et al (2009) The elements of statistical learning: data mining, inference, and prediction, vol 2. Springer Hayashi et al [2016] Hayashi A, Kameoka H, Matsubayashi T, et al (2016) Non-negative periodic component analysis for music source separation. In: 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), IEEE, pp 1–9 Ito et al [2016] Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Hastie T, Tibshirani R (1993) Varying-coefficient models. Journal of the Royal Statistical Society Series B: Statistical Methodology 55(4):757–779 Hastie et al [2009] Hastie T, Tibshirani R, Friedman JH, et al (2009) The elements of statistical learning: data mining, inference, and prediction, vol 2. Springer Hayashi et al [2016] Hayashi A, Kameoka H, Matsubayashi T, et al (2016) Non-negative periodic component analysis for music source separation. In: 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), IEEE, pp 1–9 Ito et al [2016] Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Hastie T, Tibshirani R, Friedman JH, et al (2009) The elements of statistical learning: data mining, inference, and prediction, vol 2. Springer Hayashi et al [2016] Hayashi A, Kameoka H, Matsubayashi T, et al (2016) Non-negative periodic component analysis for music source separation. In: 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), IEEE, pp 1–9 Ito et al [2016] Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Hayashi A, Kameoka H, Matsubayashi T, et al (2016) Non-negative periodic component analysis for music source separation. In: 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), IEEE, pp 1–9 Ito et al [2016] Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822
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In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Cichocki A, Amari Si (2010) Families of alpha-beta-and gamma-divergences: Flexible and robust measures of similarities. Entropy 12(6):1532–1568 Čopar et al [2017] Čopar A, Žitnik M, Zupan B (2017) Scalable non-negative matrix tri-factorization. BioData mining 10:1–16 Ding et al [2006] Ding C, Li T, Peng W, et al (2006) Orthogonal nonnegative matrix t-factorizations for clustering. In: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 126–135 Févotte et al [2009] Févotte C, Bertin N, Durrieu JL (2009) Nonnegative matrix factorization with the itakura-saito divergence: With application to music analysis. Neural computation 21(3):793–830 Gaujoux and Seoighe [2010] Gaujoux R, Seoighe C (2010) A flexible r package for nonnegative matrix factorization. BMC bioinformatics 11(1):1–9 Gillis [2020] Gillis N (2020) Nonnegative matrix factorization. SIAM Hastie and Tibshirani [1993] Hastie T, Tibshirani R (1993) Varying-coefficient models. Journal of the Royal Statistical Society Series B: Statistical Methodology 55(4):757–779 Hastie et al [2009] Hastie T, Tibshirani R, Friedman JH, et al (2009) The elements of statistical learning: data mining, inference, and prediction, vol 2. Springer Hayashi et al [2016] Hayashi A, Kameoka H, Matsubayashi T, et al (2016) Non-negative periodic component analysis for music source separation. In: 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), IEEE, pp 1–9 Ito et al [2016] Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Čopar A, Žitnik M, Zupan B (2017) Scalable non-negative matrix tri-factorization. BioData mining 10:1–16 Ding et al [2006] Ding C, Li T, Peng W, et al (2006) Orthogonal nonnegative matrix t-factorizations for clustering. In: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 126–135 Févotte et al [2009] Févotte C, Bertin N, Durrieu JL (2009) Nonnegative matrix factorization with the itakura-saito divergence: With application to music analysis. Neural computation 21(3):793–830 Gaujoux and Seoighe [2010] Gaujoux R, Seoighe C (2010) A flexible r package for nonnegative matrix factorization. BMC bioinformatics 11(1):1–9 Gillis [2020] Gillis N (2020) Nonnegative matrix factorization. SIAM Hastie and Tibshirani [1993] Hastie T, Tibshirani R (1993) Varying-coefficient models. Journal of the Royal Statistical Society Series B: Statistical Methodology 55(4):757–779 Hastie et al [2009] Hastie T, Tibshirani R, Friedman JH, et al (2009) The elements of statistical learning: data mining, inference, and prediction, vol 2. Springer Hayashi et al [2016] Hayashi A, Kameoka H, Matsubayashi T, et al (2016) Non-negative periodic component analysis for music source separation. In: 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), IEEE, pp 1–9 Ito et al [2016] Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Ding C, Li T, Peng W, et al (2006) Orthogonal nonnegative matrix t-factorizations for clustering. In: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 126–135 Févotte et al [2009] Févotte C, Bertin N, Durrieu JL (2009) Nonnegative matrix factorization with the itakura-saito divergence: With application to music analysis. Neural computation 21(3):793–830 Gaujoux and Seoighe [2010] Gaujoux R, Seoighe C (2010) A flexible r package for nonnegative matrix factorization. BMC bioinformatics 11(1):1–9 Gillis [2020] Gillis N (2020) Nonnegative matrix factorization. SIAM Hastie and Tibshirani [1993] Hastie T, Tibshirani R (1993) Varying-coefficient models. Journal of the Royal Statistical Society Series B: Statistical Methodology 55(4):757–779 Hastie et al [2009] Hastie T, Tibshirani R, Friedman JH, et al (2009) The elements of statistical learning: data mining, inference, and prediction, vol 2. Springer Hayashi et al [2016] Hayashi A, Kameoka H, Matsubayashi T, et al (2016) Non-negative periodic component analysis for music source separation. In: 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), IEEE, pp 1–9 Ito et al [2016] Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Févotte C, Bertin N, Durrieu JL (2009) Nonnegative matrix factorization with the itakura-saito divergence: With application to music analysis. Neural computation 21(3):793–830 Gaujoux and Seoighe [2010] Gaujoux R, Seoighe C (2010) A flexible r package for nonnegative matrix factorization. BMC bioinformatics 11(1):1–9 Gillis [2020] Gillis N (2020) Nonnegative matrix factorization. SIAM Hastie and Tibshirani [1993] Hastie T, Tibshirani R (1993) Varying-coefficient models. Journal of the Royal Statistical Society Series B: Statistical Methodology 55(4):757–779 Hastie et al [2009] Hastie T, Tibshirani R, Friedman JH, et al (2009) The elements of statistical learning: data mining, inference, and prediction, vol 2. Springer Hayashi et al [2016] Hayashi A, Kameoka H, Matsubayashi T, et al (2016) Non-negative periodic component analysis for music source separation. In: 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), IEEE, pp 1–9 Ito et al [2016] Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Gaujoux R, Seoighe C (2010) A flexible r package for nonnegative matrix factorization. BMC bioinformatics 11(1):1–9 Gillis [2020] Gillis N (2020) Nonnegative matrix factorization. SIAM Hastie and Tibshirani [1993] Hastie T, Tibshirani R (1993) Varying-coefficient models. Journal of the Royal Statistical Society Series B: Statistical Methodology 55(4):757–779 Hastie et al [2009] Hastie T, Tibshirani R, Friedman JH, et al (2009) The elements of statistical learning: data mining, inference, and prediction, vol 2. Springer Hayashi et al [2016] Hayashi A, Kameoka H, Matsubayashi T, et al (2016) Non-negative periodic component analysis for music source separation. In: 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), IEEE, pp 1–9 Ito et al [2016] Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Gillis N (2020) Nonnegative matrix factorization. SIAM Hastie and Tibshirani [1993] Hastie T, Tibshirani R (1993) Varying-coefficient models. Journal of the Royal Statistical Society Series B: Statistical Methodology 55(4):757–779 Hastie et al [2009] Hastie T, Tibshirani R, Friedman JH, et al (2009) The elements of statistical learning: data mining, inference, and prediction, vol 2. Springer Hayashi et al [2016] Hayashi A, Kameoka H, Matsubayashi T, et al (2016) Non-negative periodic component analysis for music source separation. In: 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), IEEE, pp 1–9 Ito et al [2016] Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Hastie T, Tibshirani R (1993) Varying-coefficient models. Journal of the Royal Statistical Society Series B: Statistical Methodology 55(4):757–779 Hastie et al [2009] Hastie T, Tibshirani R, Friedman JH, et al (2009) The elements of statistical learning: data mining, inference, and prediction, vol 2. Springer Hayashi et al [2016] Hayashi A, Kameoka H, Matsubayashi T, et al (2016) Non-negative periodic component analysis for music source separation. In: 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), IEEE, pp 1–9 Ito et al [2016] Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Hastie T, Tibshirani R, Friedman JH, et al (2009) The elements of statistical learning: data mining, inference, and prediction, vol 2. Springer Hayashi et al [2016] Hayashi A, Kameoka H, Matsubayashi T, et al (2016) Non-negative periodic component analysis for music source separation. In: 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), IEEE, pp 1–9 Ito et al [2016] Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Hayashi A, Kameoka H, Matsubayashi T, et al (2016) Non-negative periodic component analysis for music source separation. In: 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), IEEE, pp 1–9 Ito et al [2016] Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822
- Cichocki A, Amari Si (2010) Families of alpha-beta-and gamma-divergences: Flexible and robust measures of similarities. Entropy 12(6):1532–1568 Čopar et al [2017] Čopar A, Žitnik M, Zupan B (2017) Scalable non-negative matrix tri-factorization. BioData mining 10:1–16 Ding et al [2006] Ding C, Li T, Peng W, et al (2006) Orthogonal nonnegative matrix t-factorizations for clustering. In: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 126–135 Févotte et al [2009] Févotte C, Bertin N, Durrieu JL (2009) Nonnegative matrix factorization with the itakura-saito divergence: With application to music analysis. Neural computation 21(3):793–830 Gaujoux and Seoighe [2010] Gaujoux R, Seoighe C (2010) A flexible r package for nonnegative matrix factorization. BMC bioinformatics 11(1):1–9 Gillis [2020] Gillis N (2020) Nonnegative matrix factorization. SIAM Hastie and Tibshirani [1993] Hastie T, Tibshirani R (1993) Varying-coefficient models. Journal of the Royal Statistical Society Series B: Statistical Methodology 55(4):757–779 Hastie et al [2009] Hastie T, Tibshirani R, Friedman JH, et al (2009) The elements of statistical learning: data mining, inference, and prediction, vol 2. Springer Hayashi et al [2016] Hayashi A, Kameoka H, Matsubayashi T, et al (2016) Non-negative periodic component analysis for music source separation. In: 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), IEEE, pp 1–9 Ito et al [2016] Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Čopar A, Žitnik M, Zupan B (2017) Scalable non-negative matrix tri-factorization. BioData mining 10:1–16 Ding et al [2006] Ding C, Li T, Peng W, et al (2006) Orthogonal nonnegative matrix t-factorizations for clustering. In: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 126–135 Févotte et al [2009] Févotte C, Bertin N, Durrieu JL (2009) Nonnegative matrix factorization with the itakura-saito divergence: With application to music analysis. Neural computation 21(3):793–830 Gaujoux and Seoighe [2010] Gaujoux R, Seoighe C (2010) A flexible r package for nonnegative matrix factorization. BMC bioinformatics 11(1):1–9 Gillis [2020] Gillis N (2020) Nonnegative matrix factorization. SIAM Hastie and Tibshirani [1993] Hastie T, Tibshirani R (1993) Varying-coefficient models. Journal of the Royal Statistical Society Series B: Statistical Methodology 55(4):757–779 Hastie et al [2009] Hastie T, Tibshirani R, Friedman JH, et al (2009) The elements of statistical learning: data mining, inference, and prediction, vol 2. Springer Hayashi et al [2016] Hayashi A, Kameoka H, Matsubayashi T, et al (2016) Non-negative periodic component analysis for music source separation. In: 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), IEEE, pp 1–9 Ito et al [2016] Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Ding C, Li T, Peng W, et al (2006) Orthogonal nonnegative matrix t-factorizations for clustering. In: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 126–135 Févotte et al [2009] Févotte C, Bertin N, Durrieu JL (2009) Nonnegative matrix factorization with the itakura-saito divergence: With application to music analysis. Neural computation 21(3):793–830 Gaujoux and Seoighe [2010] Gaujoux R, Seoighe C (2010) A flexible r package for nonnegative matrix factorization. BMC bioinformatics 11(1):1–9 Gillis [2020] Gillis N (2020) Nonnegative matrix factorization. SIAM Hastie and Tibshirani [1993] Hastie T, Tibshirani R (1993) Varying-coefficient models. Journal of the Royal Statistical Society Series B: Statistical Methodology 55(4):757–779 Hastie et al [2009] Hastie T, Tibshirani R, Friedman JH, et al (2009) The elements of statistical learning: data mining, inference, and prediction, vol 2. Springer Hayashi et al [2016] Hayashi A, Kameoka H, Matsubayashi T, et al (2016) Non-negative periodic component analysis for music source separation. In: 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), IEEE, pp 1–9 Ito et al [2016] Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Févotte C, Bertin N, Durrieu JL (2009) Nonnegative matrix factorization with the itakura-saito divergence: With application to music analysis. Neural computation 21(3):793–830 Gaujoux and Seoighe [2010] Gaujoux R, Seoighe C (2010) A flexible r package for nonnegative matrix factorization. BMC bioinformatics 11(1):1–9 Gillis [2020] Gillis N (2020) Nonnegative matrix factorization. SIAM Hastie and Tibshirani [1993] Hastie T, Tibshirani R (1993) Varying-coefficient models. Journal of the Royal Statistical Society Series B: Statistical Methodology 55(4):757–779 Hastie et al [2009] Hastie T, Tibshirani R, Friedman JH, et al (2009) The elements of statistical learning: data mining, inference, and prediction, vol 2. Springer Hayashi et al [2016] Hayashi A, Kameoka H, Matsubayashi T, et al (2016) Non-negative periodic component analysis for music source separation. In: 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), IEEE, pp 1–9 Ito et al [2016] Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Gaujoux R, Seoighe C (2010) A flexible r package for nonnegative matrix factorization. BMC bioinformatics 11(1):1–9 Gillis [2020] Gillis N (2020) Nonnegative matrix factorization. SIAM Hastie and Tibshirani [1993] Hastie T, Tibshirani R (1993) Varying-coefficient models. Journal of the Royal Statistical Society Series B: Statistical Methodology 55(4):757–779 Hastie et al [2009] Hastie T, Tibshirani R, Friedman JH, et al (2009) The elements of statistical learning: data mining, inference, and prediction, vol 2. Springer Hayashi et al [2016] Hayashi A, Kameoka H, Matsubayashi T, et al (2016) Non-negative periodic component analysis for music source separation. In: 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), IEEE, pp 1–9 Ito et al [2016] Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Gillis N (2020) Nonnegative matrix factorization. SIAM Hastie and Tibshirani [1993] Hastie T, Tibshirani R (1993) Varying-coefficient models. Journal of the Royal Statistical Society Series B: Statistical Methodology 55(4):757–779 Hastie et al [2009] Hastie T, Tibshirani R, Friedman JH, et al (2009) The elements of statistical learning: data mining, inference, and prediction, vol 2. Springer Hayashi et al [2016] Hayashi A, Kameoka H, Matsubayashi T, et al (2016) Non-negative periodic component analysis for music source separation. In: 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), IEEE, pp 1–9 Ito et al [2016] Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Hastie T, Tibshirani R (1993) Varying-coefficient models. Journal of the Royal Statistical Society Series B: Statistical Methodology 55(4):757–779 Hastie et al [2009] Hastie T, Tibshirani R, Friedman JH, et al (2009) The elements of statistical learning: data mining, inference, and prediction, vol 2. Springer Hayashi et al [2016] Hayashi A, Kameoka H, Matsubayashi T, et al (2016) Non-negative periodic component analysis for music source separation. In: 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), IEEE, pp 1–9 Ito et al [2016] Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Hastie T, Tibshirani R, Friedman JH, et al (2009) The elements of statistical learning: data mining, inference, and prediction, vol 2. Springer Hayashi et al [2016] Hayashi A, Kameoka H, Matsubayashi T, et al (2016) Non-negative periodic component analysis for music source separation. In: 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), IEEE, pp 1–9 Ito et al [2016] Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Hayashi A, Kameoka H, Matsubayashi T, et al (2016) Non-negative periodic component analysis for music source separation. In: 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), IEEE, pp 1–9 Ito et al [2016] Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822
- Čopar A, Žitnik M, Zupan B (2017) Scalable non-negative matrix tri-factorization. BioData mining 10:1–16 Ding et al [2006] Ding C, Li T, Peng W, et al (2006) Orthogonal nonnegative matrix t-factorizations for clustering. In: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 126–135 Févotte et al [2009] Févotte C, Bertin N, Durrieu JL (2009) Nonnegative matrix factorization with the itakura-saito divergence: With application to music analysis. Neural computation 21(3):793–830 Gaujoux and Seoighe [2010] Gaujoux R, Seoighe C (2010) A flexible r package for nonnegative matrix factorization. BMC bioinformatics 11(1):1–9 Gillis [2020] Gillis N (2020) Nonnegative matrix factorization. SIAM Hastie and Tibshirani [1993] Hastie T, Tibshirani R (1993) Varying-coefficient models. Journal of the Royal Statistical Society Series B: Statistical Methodology 55(4):757–779 Hastie et al [2009] Hastie T, Tibshirani R, Friedman JH, et al (2009) The elements of statistical learning: data mining, inference, and prediction, vol 2. Springer Hayashi et al [2016] Hayashi A, Kameoka H, Matsubayashi T, et al (2016) Non-negative periodic component analysis for music source separation. In: 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), IEEE, pp 1–9 Ito et al [2016] Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Ding C, Li T, Peng W, et al (2006) Orthogonal nonnegative matrix t-factorizations for clustering. In: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 126–135 Févotte et al [2009] Févotte C, Bertin N, Durrieu JL (2009) Nonnegative matrix factorization with the itakura-saito divergence: With application to music analysis. Neural computation 21(3):793–830 Gaujoux and Seoighe [2010] Gaujoux R, Seoighe C (2010) A flexible r package for nonnegative matrix factorization. BMC bioinformatics 11(1):1–9 Gillis [2020] Gillis N (2020) Nonnegative matrix factorization. SIAM Hastie and Tibshirani [1993] Hastie T, Tibshirani R (1993) Varying-coefficient models. Journal of the Royal Statistical Society Series B: Statistical Methodology 55(4):757–779 Hastie et al [2009] Hastie T, Tibshirani R, Friedman JH, et al (2009) The elements of statistical learning: data mining, inference, and prediction, vol 2. Springer Hayashi et al [2016] Hayashi A, Kameoka H, Matsubayashi T, et al (2016) Non-negative periodic component analysis for music source separation. In: 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), IEEE, pp 1–9 Ito et al [2016] Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Févotte C, Bertin N, Durrieu JL (2009) Nonnegative matrix factorization with the itakura-saito divergence: With application to music analysis. Neural computation 21(3):793–830 Gaujoux and Seoighe [2010] Gaujoux R, Seoighe C (2010) A flexible r package for nonnegative matrix factorization. BMC bioinformatics 11(1):1–9 Gillis [2020] Gillis N (2020) Nonnegative matrix factorization. SIAM Hastie and Tibshirani [1993] Hastie T, Tibshirani R (1993) Varying-coefficient models. Journal of the Royal Statistical Society Series B: Statistical Methodology 55(4):757–779 Hastie et al [2009] Hastie T, Tibshirani R, Friedman JH, et al (2009) The elements of statistical learning: data mining, inference, and prediction, vol 2. Springer Hayashi et al [2016] Hayashi A, Kameoka H, Matsubayashi T, et al (2016) Non-negative periodic component analysis for music source separation. In: 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), IEEE, pp 1–9 Ito et al [2016] Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Gaujoux R, Seoighe C (2010) A flexible r package for nonnegative matrix factorization. BMC bioinformatics 11(1):1–9 Gillis [2020] Gillis N (2020) Nonnegative matrix factorization. SIAM Hastie and Tibshirani [1993] Hastie T, Tibshirani R (1993) Varying-coefficient models. Journal of the Royal Statistical Society Series B: Statistical Methodology 55(4):757–779 Hastie et al [2009] Hastie T, Tibshirani R, Friedman JH, et al (2009) The elements of statistical learning: data mining, inference, and prediction, vol 2. Springer Hayashi et al [2016] Hayashi A, Kameoka H, Matsubayashi T, et al (2016) Non-negative periodic component analysis for music source separation. In: 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), IEEE, pp 1–9 Ito et al [2016] Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Gillis N (2020) Nonnegative matrix factorization. SIAM Hastie and Tibshirani [1993] Hastie T, Tibshirani R (1993) Varying-coefficient models. Journal of the Royal Statistical Society Series B: Statistical Methodology 55(4):757–779 Hastie et al [2009] Hastie T, Tibshirani R, Friedman JH, et al (2009) The elements of statistical learning: data mining, inference, and prediction, vol 2. Springer Hayashi et al [2016] Hayashi A, Kameoka H, Matsubayashi T, et al (2016) Non-negative periodic component analysis for music source separation. In: 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), IEEE, pp 1–9 Ito et al [2016] Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Hastie T, Tibshirani R (1993) Varying-coefficient models. Journal of the Royal Statistical Society Series B: Statistical Methodology 55(4):757–779 Hastie et al [2009] Hastie T, Tibshirani R, Friedman JH, et al (2009) The elements of statistical learning: data mining, inference, and prediction, vol 2. Springer Hayashi et al [2016] Hayashi A, Kameoka H, Matsubayashi T, et al (2016) Non-negative periodic component analysis for music source separation. In: 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), IEEE, pp 1–9 Ito et al [2016] Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Hastie T, Tibshirani R, Friedman JH, et al (2009) The elements of statistical learning: data mining, inference, and prediction, vol 2. Springer Hayashi et al [2016] Hayashi A, Kameoka H, Matsubayashi T, et al (2016) Non-negative periodic component analysis for music source separation. In: 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), IEEE, pp 1–9 Ito et al [2016] Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Hayashi A, Kameoka H, Matsubayashi T, et al (2016) Non-negative periodic component analysis for music source separation. In: 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), IEEE, pp 1–9 Ito et al [2016] Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822
- Ding C, Li T, Peng W, et al (2006) Orthogonal nonnegative matrix t-factorizations for clustering. In: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 126–135 Févotte et al [2009] Févotte C, Bertin N, Durrieu JL (2009) Nonnegative matrix factorization with the itakura-saito divergence: With application to music analysis. Neural computation 21(3):793–830 Gaujoux and Seoighe [2010] Gaujoux R, Seoighe C (2010) A flexible r package for nonnegative matrix factorization. BMC bioinformatics 11(1):1–9 Gillis [2020] Gillis N (2020) Nonnegative matrix factorization. SIAM Hastie and Tibshirani [1993] Hastie T, Tibshirani R (1993) Varying-coefficient models. Journal of the Royal Statistical Society Series B: Statistical Methodology 55(4):757–779 Hastie et al [2009] Hastie T, Tibshirani R, Friedman JH, et al (2009) The elements of statistical learning: data mining, inference, and prediction, vol 2. Springer Hayashi et al [2016] Hayashi A, Kameoka H, Matsubayashi T, et al (2016) Non-negative periodic component analysis for music source separation. In: 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), IEEE, pp 1–9 Ito et al [2016] Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Févotte C, Bertin N, Durrieu JL (2009) Nonnegative matrix factorization with the itakura-saito divergence: With application to music analysis. Neural computation 21(3):793–830 Gaujoux and Seoighe [2010] Gaujoux R, Seoighe C (2010) A flexible r package for nonnegative matrix factorization. BMC bioinformatics 11(1):1–9 Gillis [2020] Gillis N (2020) Nonnegative matrix factorization. SIAM Hastie and Tibshirani [1993] Hastie T, Tibshirani R (1993) Varying-coefficient models. Journal of the Royal Statistical Society Series B: Statistical Methodology 55(4):757–779 Hastie et al [2009] Hastie T, Tibshirani R, Friedman JH, et al (2009) The elements of statistical learning: data mining, inference, and prediction, vol 2. Springer Hayashi et al [2016] Hayashi A, Kameoka H, Matsubayashi T, et al (2016) Non-negative periodic component analysis for music source separation. In: 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), IEEE, pp 1–9 Ito et al [2016] Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Gaujoux R, Seoighe C (2010) A flexible r package for nonnegative matrix factorization. BMC bioinformatics 11(1):1–9 Gillis [2020] Gillis N (2020) Nonnegative matrix factorization. SIAM Hastie and Tibshirani [1993] Hastie T, Tibshirani R (1993) Varying-coefficient models. Journal of the Royal Statistical Society Series B: Statistical Methodology 55(4):757–779 Hastie et al [2009] Hastie T, Tibshirani R, Friedman JH, et al (2009) The elements of statistical learning: data mining, inference, and prediction, vol 2. Springer Hayashi et al [2016] Hayashi A, Kameoka H, Matsubayashi T, et al (2016) Non-negative periodic component analysis for music source separation. In: 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), IEEE, pp 1–9 Ito et al [2016] Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Gillis N (2020) Nonnegative matrix factorization. SIAM Hastie and Tibshirani [1993] Hastie T, Tibshirani R (1993) Varying-coefficient models. Journal of the Royal Statistical Society Series B: Statistical Methodology 55(4):757–779 Hastie et al [2009] Hastie T, Tibshirani R, Friedman JH, et al (2009) The elements of statistical learning: data mining, inference, and prediction, vol 2. Springer Hayashi et al [2016] Hayashi A, Kameoka H, Matsubayashi T, et al (2016) Non-negative periodic component analysis for music source separation. In: 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), IEEE, pp 1–9 Ito et al [2016] Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Hastie T, Tibshirani R (1993) Varying-coefficient models. Journal of the Royal Statistical Society Series B: Statistical Methodology 55(4):757–779 Hastie et al [2009] Hastie T, Tibshirani R, Friedman JH, et al (2009) The elements of statistical learning: data mining, inference, and prediction, vol 2. Springer Hayashi et al [2016] Hayashi A, Kameoka H, Matsubayashi T, et al (2016) Non-negative periodic component analysis for music source separation. In: 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), IEEE, pp 1–9 Ito et al [2016] Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Hastie T, Tibshirani R, Friedman JH, et al (2009) The elements of statistical learning: data mining, inference, and prediction, vol 2. Springer Hayashi et al [2016] Hayashi A, Kameoka H, Matsubayashi T, et al (2016) Non-negative periodic component analysis for music source separation. In: 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), IEEE, pp 1–9 Ito et al [2016] Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Hayashi A, Kameoka H, Matsubayashi T, et al (2016) Non-negative periodic component analysis for music source separation. In: 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), IEEE, pp 1–9 Ito et al [2016] Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822
- Févotte C, Bertin N, Durrieu JL (2009) Nonnegative matrix factorization with the itakura-saito divergence: With application to music analysis. Neural computation 21(3):793–830 Gaujoux and Seoighe [2010] Gaujoux R, Seoighe C (2010) A flexible r package for nonnegative matrix factorization. BMC bioinformatics 11(1):1–9 Gillis [2020] Gillis N (2020) Nonnegative matrix factorization. SIAM Hastie and Tibshirani [1993] Hastie T, Tibshirani R (1993) Varying-coefficient models. Journal of the Royal Statistical Society Series B: Statistical Methodology 55(4):757–779 Hastie et al [2009] Hastie T, Tibshirani R, Friedman JH, et al (2009) The elements of statistical learning: data mining, inference, and prediction, vol 2. Springer Hayashi et al [2016] Hayashi A, Kameoka H, Matsubayashi T, et al (2016) Non-negative periodic component analysis for music source separation. In: 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), IEEE, pp 1–9 Ito et al [2016] Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Gaujoux R, Seoighe C (2010) A flexible r package for nonnegative matrix factorization. BMC bioinformatics 11(1):1–9 Gillis [2020] Gillis N (2020) Nonnegative matrix factorization. SIAM Hastie and Tibshirani [1993] Hastie T, Tibshirani R (1993) Varying-coefficient models. Journal of the Royal Statistical Society Series B: Statistical Methodology 55(4):757–779 Hastie et al [2009] Hastie T, Tibshirani R, Friedman JH, et al (2009) The elements of statistical learning: data mining, inference, and prediction, vol 2. Springer Hayashi et al [2016] Hayashi A, Kameoka H, Matsubayashi T, et al (2016) Non-negative periodic component analysis for music source separation. In: 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), IEEE, pp 1–9 Ito et al [2016] Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Gillis N (2020) Nonnegative matrix factorization. SIAM Hastie and Tibshirani [1993] Hastie T, Tibshirani R (1993) Varying-coefficient models. Journal of the Royal Statistical Society Series B: Statistical Methodology 55(4):757–779 Hastie et al [2009] Hastie T, Tibshirani R, Friedman JH, et al (2009) The elements of statistical learning: data mining, inference, and prediction, vol 2. Springer Hayashi et al [2016] Hayashi A, Kameoka H, Matsubayashi T, et al (2016) Non-negative periodic component analysis for music source separation. In: 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), IEEE, pp 1–9 Ito et al [2016] Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Hastie T, Tibshirani R (1993) Varying-coefficient models. Journal of the Royal Statistical Society Series B: Statistical Methodology 55(4):757–779 Hastie et al [2009] Hastie T, Tibshirani R, Friedman JH, et al (2009) The elements of statistical learning: data mining, inference, and prediction, vol 2. Springer Hayashi et al [2016] Hayashi A, Kameoka H, Matsubayashi T, et al (2016) Non-negative periodic component analysis for music source separation. In: 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), IEEE, pp 1–9 Ito et al [2016] Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Hastie T, Tibshirani R, Friedman JH, et al (2009) The elements of statistical learning: data mining, inference, and prediction, vol 2. Springer Hayashi et al [2016] Hayashi A, Kameoka H, Matsubayashi T, et al (2016) Non-negative periodic component analysis for music source separation. In: 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), IEEE, pp 1–9 Ito et al [2016] Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Hayashi A, Kameoka H, Matsubayashi T, et al (2016) Non-negative periodic component analysis for music source separation. In: 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), IEEE, pp 1–9 Ito et al [2016] Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822
- Gaujoux R, Seoighe C (2010) A flexible r package for nonnegative matrix factorization. BMC bioinformatics 11(1):1–9 Gillis [2020] Gillis N (2020) Nonnegative matrix factorization. SIAM Hastie and Tibshirani [1993] Hastie T, Tibshirani R (1993) Varying-coefficient models. Journal of the Royal Statistical Society Series B: Statistical Methodology 55(4):757–779 Hastie et al [2009] Hastie T, Tibshirani R, Friedman JH, et al (2009) The elements of statistical learning: data mining, inference, and prediction, vol 2. Springer Hayashi et al [2016] Hayashi A, Kameoka H, Matsubayashi T, et al (2016) Non-negative periodic component analysis for music source separation. In: 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), IEEE, pp 1–9 Ito et al [2016] Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Gillis N (2020) Nonnegative matrix factorization. SIAM Hastie and Tibshirani [1993] Hastie T, Tibshirani R (1993) Varying-coefficient models. Journal of the Royal Statistical Society Series B: Statistical Methodology 55(4):757–779 Hastie et al [2009] Hastie T, Tibshirani R, Friedman JH, et al (2009) The elements of statistical learning: data mining, inference, and prediction, vol 2. Springer Hayashi et al [2016] Hayashi A, Kameoka H, Matsubayashi T, et al (2016) Non-negative periodic component analysis for music source separation. In: 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), IEEE, pp 1–9 Ito et al [2016] Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Hastie T, Tibshirani R (1993) Varying-coefficient models. Journal of the Royal Statistical Society Series B: Statistical Methodology 55(4):757–779 Hastie et al [2009] Hastie T, Tibshirani R, Friedman JH, et al (2009) The elements of statistical learning: data mining, inference, and prediction, vol 2. Springer Hayashi et al [2016] Hayashi A, Kameoka H, Matsubayashi T, et al (2016) Non-negative periodic component analysis for music source separation. In: 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), IEEE, pp 1–9 Ito et al [2016] Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Hastie T, Tibshirani R, Friedman JH, et al (2009) The elements of statistical learning: data mining, inference, and prediction, vol 2. Springer Hayashi et al [2016] Hayashi A, Kameoka H, Matsubayashi T, et al (2016) Non-negative periodic component analysis for music source separation. In: 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), IEEE, pp 1–9 Ito et al [2016] Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Hayashi A, Kameoka H, Matsubayashi T, et al (2016) Non-negative periodic component analysis for music source separation. In: 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), IEEE, pp 1–9 Ito et al [2016] Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822
- Gillis N (2020) Nonnegative matrix factorization. SIAM Hastie and Tibshirani [1993] Hastie T, Tibshirani R (1993) Varying-coefficient models. Journal of the Royal Statistical Society Series B: Statistical Methodology 55(4):757–779 Hastie et al [2009] Hastie T, Tibshirani R, Friedman JH, et al (2009) The elements of statistical learning: data mining, inference, and prediction, vol 2. Springer Hayashi et al [2016] Hayashi A, Kameoka H, Matsubayashi T, et al (2016) Non-negative periodic component analysis for music source separation. In: 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), IEEE, pp 1–9 Ito et al [2016] Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Hastie T, Tibshirani R (1993) Varying-coefficient models. Journal of the Royal Statistical Society Series B: Statistical Methodology 55(4):757–779 Hastie et al [2009] Hastie T, Tibshirani R, Friedman JH, et al (2009) The elements of statistical learning: data mining, inference, and prediction, vol 2. Springer Hayashi et al [2016] Hayashi A, Kameoka H, Matsubayashi T, et al (2016) Non-negative periodic component analysis for music source separation. In: 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), IEEE, pp 1–9 Ito et al [2016] Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Hastie T, Tibshirani R, Friedman JH, et al (2009) The elements of statistical learning: data mining, inference, and prediction, vol 2. Springer Hayashi et al [2016] Hayashi A, Kameoka H, Matsubayashi T, et al (2016) Non-negative periodic component analysis for music source separation. In: 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), IEEE, pp 1–9 Ito et al [2016] Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Hayashi A, Kameoka H, Matsubayashi T, et al (2016) Non-negative periodic component analysis for music source separation. In: 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), IEEE, pp 1–9 Ito et al [2016] Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822
- Hastie T, Tibshirani R (1993) Varying-coefficient models. Journal of the Royal Statistical Society Series B: Statistical Methodology 55(4):757–779 Hastie et al [2009] Hastie T, Tibshirani R, Friedman JH, et al (2009) The elements of statistical learning: data mining, inference, and prediction, vol 2. Springer Hayashi et al [2016] Hayashi A, Kameoka H, Matsubayashi T, et al (2016) Non-negative periodic component analysis for music source separation. In: 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), IEEE, pp 1–9 Ito et al [2016] Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Hastie T, Tibshirani R, Friedman JH, et al (2009) The elements of statistical learning: data mining, inference, and prediction, vol 2. Springer Hayashi et al [2016] Hayashi A, Kameoka H, Matsubayashi T, et al (2016) Non-negative periodic component analysis for music source separation. In: 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), IEEE, pp 1–9 Ito et al [2016] Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Hayashi A, Kameoka H, Matsubayashi T, et al (2016) Non-negative periodic component analysis for music source separation. In: 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), IEEE, pp 1–9 Ito et al [2016] Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822
- Hastie T, Tibshirani R, Friedman JH, et al (2009) The elements of statistical learning: data mining, inference, and prediction, vol 2. Springer Hayashi et al [2016] Hayashi A, Kameoka H, Matsubayashi T, et al (2016) Non-negative periodic component analysis for music source separation. In: 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), IEEE, pp 1–9 Ito et al [2016] Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Hayashi A, Kameoka H, Matsubayashi T, et al (2016) Non-negative periodic component analysis for music source separation. In: 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), IEEE, pp 1–9 Ito et al [2016] Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822
- Hayashi A, Kameoka H, Matsubayashi T, et al (2016) Non-negative periodic component analysis for music source separation. In: 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), IEEE, pp 1–9 Ito et al [2016] Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822
- Ito Y, Oeda Si, Yamanishi K (2016) Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In: Proceedings Of The 2016 SIAM international conference on data mining, SIAM, pp 720–728 Lee and Seung [2000] Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822
- Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol 13. MIT Press, URL https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf Lee and Seung [1999] Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822
- Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791 Mifrah and Benlahmar [2020] Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822
- Mifrah S, Benlahmar E (2020) Topic modeling coherence: A comparative study between lda and nmf models using covid’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering pp 5756–5761 Murphy [2012] Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822
- Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press Potthoff and Roy [1964] Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822
- Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326. URL https://api.semanticscholar.org/CorpusID:23662833 R Core Team [2023] R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822
- R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/ Satoh [2023] Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822
- Satoh K (2023) On non-negative matrix factorization using gaussian kernels as covariates. Japanese Journal of Applied Statistics 52(2):59–74. 10.5023/jappstat.52.59 Satoh and Tonda [2016] Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822
- Satoh K, Tonda T (2016) Estimating regression coefficients for balanced growth curve model when time trend of baseline is not specified. American Journal of Mathematical and Management Sciences 35(3):183–193. 10.1080/01966324.2015.1137253 Satoh and Yanagihara [2010] Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822
- Satoh K, Yanagihara H (2010) Estimation of varying coefficients for a growth curve model. American Journal of Mathematical and Management Sciences 30(3-4):243–256 Satoh et al [2016] Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822
- Satoh K, Tonda T, Izumi S (2016) Logistic regression model for survival time analysis using time-varying coefficients. American Journal of Mathematical and Management Sciences 35(4):353–360 Von Rosen [1991] Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822 Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822
- Von Rosen D (1991) The growth curve model: a review. Communications in Statistics-Theory and Methods 20(9):2791–2822
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