Semi-Parametric Estimation for Conditional Independence Multivariate Finite Mixture Models - Archive ouverte HAL Accéder directement au contenu
Pré-Publication, Document De Travail Année : 2014

Semi-Parametric Estimation for Conditional Independence Multivariate Finite Mixture Models

Résumé

The conditional independence assumption for nonparametric multivariate finite mixture models, a weaker form of the well-known conditional independence assumption for random effects models for longitudinal data, is the subject of an increasing number of theoretical and algorithmic developments in the statistical literature. After presenting a survey of this literature, including an in-depth discussion of the all-important identifiability results, this article describes and extends an algorithm for estimation of the parameters in these models. The algorithm works for any number of components in three or more dimensions. It possesses a descent property and can be easily adapted to situations where the data are grouped in blocks of conditionally independent variables. We discuss how to adapt this algorithm to various location-scale models that link component densities, and we even adapt it to a particular class of univariate mixture problems in which the components are assumed symmetric. We give a bandwidth selection procedure for our algorithm. Finally, we demonstrate the effectiveness of our algorithm using a simulation study and two psychometric datasets.
Fichier principal
Vignette du fichier
SPMixEst_v2.pdf (2.24 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-00558834 , version 1 (24-01-2011)
hal-00558834 , version 2 (18-11-2014)

Identifiants

  • HAL Id : hal-00558834 , version 2

Citer

Didier Chauveau, David R. Hunter, Michael Levine. Semi-Parametric Estimation for Conditional Independence Multivariate Finite Mixture Models. 2014. ⟨hal-00558834v2⟩
175 Consultations
179 Téléchargements

Partager

Gmail Facebook X LinkedIn More