On posterior consistency of survival models. (English) Zbl 1012.62105

Summary: J.K. Ghosh and R.V. Ramamoorthi [Consistency of Bayesian inference for survival analysis with or without censoring. IMS Lect. Notes, Monogr. Ser. 27, 95-103 (1995; Zbl 0876.62021)] studied posterior consistency for survival models and showed that the posterior was consistent when the prior on the distribution of survival times was the Dirichlet process prior. We study posterior consistency of survival models with neutral to the right process priors which include Dirichlet process priors. A set of sufficient conditions for posterior consistency with neutral to the right process priors is given. Interestingly, not all the neutral to the right process priors have consistent posteriors, but most of the popular priors such as Dirichlet processes, beta processes and gamma processes have consistent posteriors. With a class of priors which includes beta processes, a necessary and sufficient condition for the consistency is also established.
An interesting counter-intuitive phenomenon is found. Suppose there are two priors centered at the true parameter value with finite variances. Surprisingly, the posterior with smaller prior variance can be inconsistent, while that with larger prior variance is consistent.


62N02 Estimation in survival analysis and censored data
62N05 Reliability and life testing
62G20 Asymptotic properties of nonparametric inference
62G05 Nonparametric estimation


Zbl 0876.62021
Full Text: DOI


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