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Bayesian planning and inference of a progressively censored sample from linear hazard rate distribution

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  • Sen, Ananda
  • Kannan, Nandini
  • Kundu, Debasis

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.

Suggested Citation

  • Sen, Ananda & Kannan, Nandini & Kundu, Debasis, 2013. "Bayesian planning and inference of a progressively censored sample from linear hazard rate distribution," Computational Statistics & Data Analysis, Elsevier, vol. 62(C), pages 108-121.
    • Handle: RePEc:eee:csdana:v:62:y:2013:i:c:p:108-121
    DOI: 10.1016/j.csda.2013.01.003
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    References listed on IDEAS

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