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

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.
Document type :
Conference papers
Complete list of metadatas

https://hal.archives-ouvertes.fr/hal-01478523
Contributor : Didier Chauveau <>
Submitted on : Sunday, March 12, 2017 - 6:25:32 PM
Last modification on : Sunday, April 7, 2019 - 3:00:04 PM
Long-term archiving on: Tuesday, June 13, 2017 - 12:13:24 PM

File

Bordes_ChauveauJSM2016.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-01478523, version 1

Collections

Citation

Laurent Bordes, Didier Chauveau. Stochastic EM-like Algorithms for Fitting Finite Mixture of Lifetime Regression Models Under Right Censoring. Joint Statistical Meeting 2016, American Statistical Association, Jul 2016, Chicago, United States. pp.1735-1746. ⟨hal-01478523⟩

Share

Metrics

Record views

164

Files downloads

208