A mixture model-based approach to the clustering of exponential repeated data

Abstract : The analysis of finite mixture models for exponential repeated data is considered. The mixture components correspond to different unknown groups of the statistical units. Dependency and variability of repeated data are taken into account through random effects. For each component, an exponential mixed model is thus defined. When considering parameter estimation in this mixture of exponential mixed models, the EM-algorithm cannot be directly used since the marginal distribution of each mixture component cannot be analytically derived. In this paper, we propose two parameter estimation methods. The first one uses a linearisation specific to the exponential distribution hypothesis within each component. The second approach uses a Metropolis-Hastings algorithm as a building block of a general MCEM-algorithm
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Journal of Multivariate Analysis, Elsevier, 2009, 100 (9), pp.1938-1951. 〈10.1016/j.jmva.2009.04.006〉
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https://hal.archives-ouvertes.fr/hal-00385896
Contributeur : Marie-José Martinez <>
Soumis le : mercredi 20 mai 2009 - 13:28:50
Dernière modification le : mercredi 11 avril 2018 - 01:58:40

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Marie-José Martinez, Christian Lavergne, Catherine Trottier. A mixture model-based approach to the clustering of exponential repeated data. Journal of Multivariate Analysis, Elsevier, 2009, 100 (9), pp.1938-1951. 〈10.1016/j.jmva.2009.04.006〉. 〈hal-00385896〉

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