2005/02/28 by Man-Wai Ho
Computer Science · Mathematics · #Bayesian Methods and Mixture Models #Statistical Distribution Estimation and Applications #Statistical Methods and Bayesian Inference #math.ST #msc:62F15 #msc:62G05 #stat.TH
paper · pdf · doi:10.1214/009053606000000047
published as Annals of Statistics 2006, Vol. 34, No. 2, 820-836 · Published at http://dx.doi.org/10.1214/009053606000000047 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)
openalex publication_date 2006/04/01 · arxiv created 2006/07/01 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
A class of random hazard rates, which is defined as a mixture of an indicator kernel convolved with a completely random measure, is of interest. We provide an explicit characterization of the posterior distribution of this mixture hazard rate model via a finite mixture of S-paths. A closed and tractable Bayes estimator for the hazard rate is derived to be a finite sum over S-paths. The path characterization or the estimator is proved to be a Rao–Blackwellization of an existing partition characterization or partition-sum estimator. This accentuates the importance of S-paths in Bayesian modeling of monotone hazard rates. An efficient Markov chain Monte Carlo (MCMC) method is proposed to approximate this class of estimates. It is shown that S-path characterization also exists in modeling with covariates by a proportional hazard model, and the proposed algorithm again applies. Numerical results of the method are given to demonstrate its practicality and effectiveness.