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Modèle à processus latent et algorithme EM pour la régression non linéaire (1312.6978v1)
Published 25 Dec 2013 in math.ST, cs.LG, stat.ME, stat.ML, and stat.TH
Abstract: A non linear regression approach which consists of a specific regression model incorporating a latent process, allowing various polynomial regression models to be activated preferentially and smoothly, is introduced in this paper. The model parameters are estimated by maximum likelihood performed via a dedicated expecation-maximization (EM) algorithm. An experimental study using simulated and real data sets reveals good performances of the proposed approach.