A stochastic EM algorithm for a semiparametric mixture model

Recently, there has been a considerable interest in finite mixture models with semi-/non-parametric component distributions. Identifiability of such model parameters is generally not obvious, and when it occurs, inference methods are rather specific to the mixture model under consideration. Hence, a generalization of the EM algorithm to semiparametric mixture models is proposed. The approach is methodological and can be applied to a wide class of semiparametric mixture models. The behavior of the proposed EM type estimators is studied numerically not only through several Monte-Carlo experiments but also through comparison with alternative methods existing in the literature. In addition to these numerical experiments, applications to real data are provided, showing that the estimation method behaves well, that it is fast and easy to be implemented. © 2006 Elsevier B.V. All rights reserved.

• Type: Journal
• Year: 2007
• Where: CSDA
• Authors: Laurent Bordes, Didier Chauveau, Pierre Vandekerkhove
• Keywords: CSDA 2007, Mixture Models, Semiparametric Mixture Models, finite Mixture Models