Bayesian inference under progressive type-I interval censoring

Yu-Jau Lin and Y. L. Lio

Journal of Applied Statistics, 2012, vol. 39, issue 8, 1811-1824

Abstract: Bayesian estimation for population parameter under progressive type-I interval censoring is studied via Markov Chain Monte Carlo (MCMC) simulation. Two competitive statistical models, generalized exponential and Weibull distributions for modeling a real data set containing 112 patients with plasma cell myeloma, are studied for illustration. In model selection, a novel Bayesian procedure which involves a mixture model is proposed. Then the mix proportion is estimated through MCMC and used as the model selection criterion.

Date: 2012
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DOI: 10.1080/02664763.2012.683170

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