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Nonparametric Bayesian survival analysis using mixtures of Weibull distributions
- Source :
- Journal of Statistical Planning and Inference. 136:578-596
- Publication Year :
- 2006
- Publisher :
- Elsevier BV, 2006.
-
Abstract
- Bayesian nonparametric methods have been applied to survival analysis problems since the emergence of the area of Bayesian nonparametrics. However, the use of the flexible class of Dirichlet process mixture models has been rather limited in this context. This is, arguably, to a large extent, due to the standard way of fitting such models that precludes full posterior inference for many functionals of interest in survival analysis applications. To overcome this difficulty, we provide a computational approach to obtain the posterior distribution of general functionals of a Dirichlet process mixture. We model the survival distribution employing a flexible Dirichlet process mixture, with a Weibull kernel, that yields rich inference for several important functionals. In the process, a method for hazard function estimation emerges. Methods for simulation-based model fitting, in the presence of censoring, and for prior specification are provided. We illustrate the modeling approach with simulated and real data.
- Subjects :
- Statistics and Probability
Applied Mathematics
Posterior probability
Inference
Censoring (statistics)
Dirichlet distribution
Dirichlet process
symbols.namesake
ComputingMethodologies_PATTERNRECOGNITION
Survival function
Prior probability
Econometrics
symbols
Applied mathematics
Statistics, Probability and Uncertainty
Mathematics
Weibull distribution
Subjects
Details
- ISSN :
- 03783758
- Volume :
- 136
- Database :
- OpenAIRE
- Journal :
- Journal of Statistical Planning and Inference
- Accession number :
- edsair.doi...........38b621cbab8b04a1d153a61224308a0f
- Full Text :
- https://doi.org/10.1016/j.jspi.2004.08.009