An alternative estimation approach to fit a heterogeneity linear mixed model

Marie-José Martinez 1 Emma Holian 2
1 MISTIS - Modelling and Inference of Complex and Structured Stochastic Systems
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
Abstract : An alternative estimation approach is proposed to fit a linear mixed effects model where the random effects follow a finite mixture of normal distributions. This model, called a heterogeneity linear mixed model, is an interesting tool since it relaxes the classical normality assumption and is also perfectly suitable for classification purposes, based on longitudinal profiles. Instead of fitting directly the heterogeneity linear mixed model, we propose to fit an equivalent mixture of linear mixed models under some restrictions which is computationally simpler. Indeed, unlike the former model, the latter can be maximized analytically using an EM-algorithm and the obtained parameter estimates can be easily used to compute the parameter estimates of interest. We study and compare the behaviour of our approach on simulations. Finally, the use of our approach is illustrated on a real data set.
Type de document :
Communication dans un congrès
ERCIM 2013 - 6th International Conference of the ERCIM Working Group on Computational and Methodological Statistics, Dec 2013, Londres, United Kingdom
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https://hal.archives-ouvertes.fr/hal-00926627
Contributeur : Marie-José Martinez <>
Soumis le : jeudi 9 janvier 2014 - 22:34:46
Dernière modification le : mardi 25 août 2015 - 01:04:43

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  • HAL Id : hal-00926627, version 1

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Marie-José Martinez, Emma Holian. An alternative estimation approach to fit a heterogeneity linear mixed model. ERCIM 2013 - 6th International Conference of the ERCIM Working Group on Computational and Methodological Statistics, Dec 2013, Londres, United Kingdom. <hal-00926627>

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