PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Mixture of linear mixed models - Application to repeated data clustering
Gilles Celeux, Olivier Martin and Christian Lavergne
Staistical Modelling Volume 5, pp. 243-257, 2005.


The problem of finite mixture analysis from repeated data is considered. Data variability is taken into account through linear mixed models leading to a mixture of mixed models. The maximum likelihood estimation of this family of models through the EM algorithm is presented. The problem of selecting a particular mixture of mixed models is considered. Illustrative Monte Carlo experiments are presented and an application to the clustering of gene expression profiles is detailed. All those experiments highlight the interest of linear mixed model mixtur es for taking account of data variability in a proper way.

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EPrint Type:Article
Additional Information:Random effects Models
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:1856
Deposited By:Gilles Celeux
Deposited On:29 November 2005