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Posterior analysis of the compound Rayleigh distribution under balanced loss functions for censored data

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  • D. R. Barot
  • M. N. Patel

Abstract

This paper develops Bayesian analysis in the context of progressively Type II censored data from the compound Rayleigh distribution. The maximum likelihood and Bayes estimates along with associated posterior risks are derived for reliability performances under balanced loss functions by assuming continuous priors for parameters of the distribution. A practical example is used to illustrate the estimation methods. A simulation study has been carried out to compare the performance of estimates. The study indicates that Bayesian estimation should be preferred over maximum likelihood estimation. In Bayesian estimation, the balance general entropy loss function can be effectively employed for optimal decision-making.

Suggested Citation

  • D. R. Barot & M. N. Patel, 2017. "Posterior analysis of the compound Rayleigh distribution under balanced loss functions for censored data," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(3), pages 1317-1336, February.
    • Handle: RePEc:taf:lstaxx:v:46:y:2017:i:3:p:1317-1336
    DOI: 10.1080/03610926.2015.1019140
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    Cited by:

    1. Kousik Maiti & Suchandan Kayal, 2019. "Estimation for the generalized Fréchet distribution under progressive censoring scheme," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 10(5), pages 1276-1301, October.
    2. Mohamed A. W. Mahmoud & Mohamed G. M. Ghazal & Hossam M. M. Radwan, 2023. "Bayesian Estimation and Optimal Censoring of Inverted Generalized Linear Exponential Distribution Using Progressive First Failure Censoring," Annals of Data Science, Springer, vol. 10(2), pages 527-554, April.
    3. Kousik Maiti & Suchandan Kayal, 2023. "Estimating Reliability Characteristics of the Log-Logistic Distribution Under Progressive Censoring with Two Applications," Annals of Data Science, Springer, vol. 10(1), pages 89-128, February.

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