A Stochastic EM algorithm for a semiparametric mixture model

Abstract : Recently several authors considered 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. In this paper we propose a generalization of the EM algorithm to semiparametric mixture models. Our approach is methodological and can be applied to a wide class of semiparametric mixture models. The behavior of the EM type estimators we propose is studied numerically through several Monte Carlo experiments but also by comparison with alternative methods existing in the literature. In addition to these numerical experiments we provide applications to real data showing that our estimation methods behaves well, that it is fast and easy to be implemented.
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Article dans une revue
Computational Statistics and Data Analysis, Elsevier, 2007, 51, pp.5429-5443. 〈10.1016/j.csda.2006.08.015〉
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Contributeur : Didier Chauveau <>
Soumis le : vendredi 3 février 2006 - 10:53:39
Dernière modification le : vendredi 22 février 2019 - 10:40:03
Document(s) archivé(s) le : samedi 3 avril 2010 - 22:07:25

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Laurent Bordes, Didier Chauveau, Pierre Vandekerkhove. A Stochastic EM algorithm for a semiparametric mixture model. Computational Statistics and Data Analysis, Elsevier, 2007, 51, pp.5429-5443. 〈10.1016/j.csda.2006.08.015〉. 〈hal-00018493〉

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