 Bayesian planning and inference of a progressively censored sample from linear hazard rate distributionAnanda Sen, Nandini Kannan and Debasis Kundu Computational Statistics & Data Analysis, 2013, vol. 62, issue C, 108-121 Abstract: This paper deals with the Bayesian inference of the linear hazard rate (LHR) distribution under a progressively censoring scheme. A unified treatment of both Type I and Type II censoring is presented under independent gamma priors for the parameters, that yields the posteriors as mixtures of gamma. The priors are motivated from a probability matching viewpoint. Along with marginal inference and prediction, a joint credible set is constructed utilizing the posterior distribution of certain quantities of interest. The Bayesian inference demonstrates an intimate connection with the frequentist inference results under a Type-II censoring scheme. Bayesian planning strategies are explored that search for the optimal progressive censoring schemes under a variance criterion as well as a criterion based on the length of a credible interval for percentiles. Keywords: Linear hazard rate; Progressive censoring scheme; Bayesian inference; Markov Chain Monte Carlo; Joint credible set; Bayesian planning (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:62:y:2013:i:c:p:108-121 DOI: 10.1016/j.csda.2013.01.003 Access Statistics for this article Computational Statistics & Data Analysis is currently edited by S.P. Azen More articles in Computational Statistics & Data Analysis from Elsevier |
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