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Activity Number: 156
Type: Topic Contributed
Date/Time: Monday, August 1, 2016 : 10:30 AM to 12:20 PM
Sponsor: Section on Nonparametric Statistics
Abstract #319649
Title: Stochastic EM-like Algorithms for Fitting Finite Mixture of Lifetime Regression Models Under Right Censoring
Author(s): Laurent Bordes* and Didier Chauveau
Companies: and University of Orleans
Keywords: Right censoring ; EM algorithm ; Proportional hazards model ; Semiparametric mixture models

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.

Authors who are presenting talks have a * after their name.

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