Maximum likelihood estimation for multivariate skew normal mixture models

Authors:
Tsung I. Lin
Affiliations:
Department of Applied Mathematics, National Chung Hsing University, Taichung 402, Taiwan
Venue:
Journal of Multivariate Analysis
Year:
2009

Citing 4
Cited 14

Multivariate statistics: a practical approach

Multivariate statistics: a practical approach
A multivariate skew normal distribution

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Maximum likelihood inference for mixtures of skew Student-t-normal distributions through practical EM-type algorithms

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Flexible mixture modeling via the multivariate t distribution with the Box-Cox transformation: an alternative to the skew-t distribution

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Mixtures of common factor analyzers for high-dimensional data with missing information

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On the distribution of posterior probabilities in finite mixture models with application in clustering

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On mixtures of skew normal and skew $$t$$-distributions

Advances in Data Analysis and Classification
Using conditional independence for parsimonious model-based Gaussian clustering

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Learning from incomplete data via parameterized t mixture models through eigenvalue decomposition

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Multivariate measurement error models using finite mixtures of skew-Student t distributions

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Finite mixtures of multivariate skew t-distributions: some recent and new results

Statistics and Computing

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Abstract

This paper provides a flexible mixture modeling framework using the multivariate skew normal distribution. A feasible EM algorithm is developed for finding the maximum likelihood estimates of parameters in this context. A general information-based method for obtaining the asymptotic covariance matrix of the maximum likelihood estimators is also presented. The proposed methodology is illustrated with a real example and results are also compared with those obtained from fitting normal mixtures.