Statistics Surveys

Semi-parametric estimation for conditional independence multivariate finite mixture models

Didier Chauveau, David R. Hunter, and Michael Levine

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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.

Article information

Statist. Surv. Volume 9 (2015), 1-31.

First available in Project Euclid: 6 February 2015

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Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

Primary: 62G05: Estimation
Secondary: 62G07: Density estimation

Kernel density estimation MM algorithms


Chauveau, Didier; Hunter, David R.; Levine, Michael. Semi-parametric estimation for conditional independence multivariate finite mixture models. Statist. Surv. 9 (2015), 1--31. doi:10.1214/15-SS108.

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