Bayesian inference for Rayleigh distribution under progressive censored sample

Shuo‐Jye Wu, Dar‐Hsin Chen and Shyi‐Tien Chen

Applied Stochastic Models in Business and Industry, 2006, vol. 22, issue 3, 269-279

Abstract: It is often the case that some information is available on the parameter of failure time distributions from previous experiments or analyses of failure time data. The Bayesian approach provides the methodology for incorporation of previous information with the current data. In this paper, given a progressively type II censored sample from a Rayleigh distribution, Bayesian estimators and credible intervals are obtained for the parameter and reliability function. We also derive the Bayes predictive estimator and highest posterior density prediction interval for future observations. Two numerical examples are presented for illustration and some simulation study and comparisons are performed. Copyright © 2006 John Wiley & Sons, Ltd.

Date: 2006
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Persistent link: https://EconPapers.repec.org/RePEc:wly:apsmbi:v:22:y:2006:i:3:p:269-279

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