A0193
Title: Full model estimation for non-parametric multivariate finite mixture models
Authors: Matthieu Marbac - CREST - ENSAI (France) [presenting]
Marie du Roy de Chaumaray - Universite Rennes 2 (France)
Abstract: The problem of full model estimation for non-parametric finite mixture models is addressed. An approach is presented for selecting the number of components and the subset of discriminative variables (i.e., the subset of variables having different distributions among the mixture components). The proposed approach considers a discretization of each variable into $B$ bins and a penalization of the resulting log-likelihood. Considering that the number of bins tends to infinity as the sample size tends to infinity, we prove that our estimator of the model (number of components and subset of relevant variables for clustering) is consistent under a suitable choice of the penalty term. The interest of our proposal is illustrated on simulated and benchmark data.
