Stochastic EM-like Algorithms for Fitting Finite Mixture of Lifetime Regression Models Under Right Censoring

Laurent Bordes 1 Didier Chauveau 2
1 LMAP - Laboratoire de Mathématiques et de leurs Applications [Pau]
2 MAPMO - Mathématiques - Analyse, Probabilités, Modélisation - Orléans
Abstract : Finite mixture of models based on the proportional hazards or the accelerated failure time assumption lead to a large variety of lifetime regression models. We present several iterative methods based on EM and Stochastic EM methodologies, that allow fitting parametric or semiparametric mixture of lifetime regression models for randomly right censored lifetime data including covariates. Their identifiability is briefly discussed and in the semiparametric case we show that simulating the missing data coming from the mixture allows to use the ordinary partial likelihood inference method in an EM algorithm's M-step. The effectiveness of the new proposed algorithms is illustrated through simulation studies.
Keywords : Right censoring EM algorithm proportional hazards model semiparametric mixture models
Type de document :
Communication dans un congrès
Alexandria, VA: American Statistical Association. Joint Statistical Meeting 2016, Jul 2016, Chicago, United States. JSM Proceedings, Section on Nonparametric Statistics, pp.1735-1746, 2016, <https://ww2.amstat.org/meetings/jsm/2016/>
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https://hal.archives-ouvertes.fr/hal-01478523
Contributeur : Didier Chauveau <>
Soumis le : dimanche 12 mars 2017 - 18:25:32
Dernière modification le : mercredi 15 mars 2017 - 01:03:03

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Laurent Bordes, Didier Chauveau. Stochastic EM-like Algorithms for Fitting Finite Mixture of Lifetime Regression Models Under Right Censoring. Alexandria, VA: American Statistical Association. Joint Statistical Meeting 2016, Jul 2016, Chicago, United States. JSM Proceedings, Section on Nonparametric Statistics, pp.1735-1746, 2016, <https://ww2.amstat.org/meetings/jsm/2016/>. <hal-01478523>

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