A stochastic EM algorithm for a semiparametric mixture model
| Autor: | Pierre Vandekerkhove, Laurent Bordes, Didier Chauveau |
|---|---|
| Přispěvatelé: | Laboratoire de Mathématiques Appliquées de Compiègne (LMAC), Université de Technologie de Compiègne (UTC), Mathématiques - Analyse, Probabilités, Modélisation - Orléans (MAPMO), Centre National de la Recherche Scientifique (CNRS)-Université d'Orléans (UO), Laboratoire d'Analyse et de Mathématiques Appliquées (LAMA), Centre National de la Recherche Scientifique (CNRS)-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-Fédération de Recherche Bézout-Université Paris-Est Marne-la-Vallée (UPEM), Université d'Orléans (UO)-Centre National de la Recherche Scientifique (CNRS), Université Paris-Est Marne-la-Vallée (UPEM)-Fédération de Recherche Bézout-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-Centre National de la Recherche Scientifique (CNRS) |
| Rok vydání: | 2007 |
| Předmět: |
Statistics and Probability
Generalization 02 engineering and technology 01 natural sciences semiparametric model 010104 statistics & probability [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST] Expectation–maximization algorithm 0202 electrical engineering electronic engineering information engineering Calculus Applied mathematics Semiparametric regression 0101 mathematics EM algorithm AMS 2000 subject Classification. 62G05 62G07 62F10 Mathematics Applied Mathematics Estimator 020206 networking & telecommunications Mixture model Semiparametric model Computational Mathematics Computational Theory and Mathematics Parametric model Identifiability finite mixture model |
| Zdroj: | Computational Statistics and Data Analysis Computational Statistics and Data Analysis, Elsevier, 2007, 51, pp.5429-5443. ⟨10.1016/j.csda.2006.08.015⟩ |
| ISSN: | 0167-9473 |
| DOI: | 10.1016/j.csda.2006.08.015 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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