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Bayesian inference for Rayleigh distribution under progressive censored sample

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  • Shuo‐Jye Wu
  • Dar‐Hsin Chen
  • Shyi‐Tien Chen

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.

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  • Shuo‐Jye Wu & Dar‐Hsin Chen & Shyi‐Tien Chen, 2006. "Bayesian inference for Rayleigh distribution under progressive censored sample," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 22(3), pages 269-279, May.
    • Handle: RePEc:wly:apsmbi:v:22:y:2006:i:3:p:269-279
    DOI: 10.1002/asmb.615
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    Cited by:

    1. M. El-Din & A. Shafay, 2013. "One- and two-sample Bayesian prediction intervals based on progressively Type-II censored data," Statistical Papers, Springer, vol. 54(2), pages 287-307, May.
    2. Wu, Shuo-Jye & Kus, Coskun, 2009. "On estimation based on progressive first-failure-censored sampling," Computational Statistics & Data Analysis, Elsevier, vol. 53(10), pages 3659-3670, August.

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