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- W2003751510 abstract "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." @default.
- W2003751510 created "2016-06-24" @default.
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- W2003751510 date "2006-04-01" @default.
- W2003751510 modified "2023-10-18" @default.
- W2003751510 title "A Bayes method for a monotone hazard rate via S-paths" @default.
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- W2003751510 doi "https://doi.org/10.1214/009053606000000047" @default.
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