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

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.