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EM and Stochastic EM algorithms for reliability mixture models under random censoring

Laurent Bordes 1, Didier Chauveau 2

(2012-04-06)

We present in this paper several iterative methods based on EM and Stochastic EM methodology, that allow to estimate parametric or semiparametric mixture models for randomly right censored lifetime data, provided they are identifiable. We consider different levels of completion for the (incomplete) observed data, and provide genuine or EM-like algorithms for several situations. In particular, we show that in censored semiparametric situations, a stochastic step is the only practical solution allowing computation of nonparametric estimates of the unknown survival function. The effectiveness of the new proposed algorithms is demonstrated in simulation studies.

1:	Laboratoire de Mathématiques et de leurs Applications [Pau] (LMAP)
	CNRS : UMR5142 – Université de Pau et des Pays de l'Adour [UPPA]
2:	Mathématiques - Analyse, Probabilités, Modélisation - Orléans (MAPMO)
	Université d'Orléans – CNRS : UMR7349

Subject

:

Mathematics/Statistics Statistics/Statistics Theory

Keyword(s): EM algorithm – finite mixture – semiparametric mixtures – reliability – survival data

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From: Didier Chauveau <>

Submitted on: Friday, 6 April 2012 10:17:09

Updated on: Friday, 6 April 2012 23:02:21