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Bayesian inference for two log-logistic populations under joint progressive type II censoring schemes

Author

Listed:
  • Ranjita Pandey

    (University of Delhi)

  • Pulkit Srivastava

    (University of Delhi)

Abstract

In this paper, we have discussed classical and Bayesian estimation of combined parameters of two different log-logistic models under a new type of censoring scheme known as joint progressive type II censoring scheme considering different scale parameters and common shape parameters. Maximum likelihood estimators are constructed with asymptotic confidence intervals. Then, Bayes estimators of parameters are proposed under different loss functions along with credible intervals and highest posterior density intervals. Markov Chain Monte Carlo approximation method has been used for simulation purpose. A real dataset has also been discussed for illustration.

Suggested Citation

  • Ranjita Pandey & Pulkit Srivastava, 2022. "Bayesian inference for two log-logistic populations under joint progressive type II censoring schemes," 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. 13(6), pages 2981-2991, December.
    • Handle: RePEc:spr:ijsaem:v:13:y:2022:i:6:d:10.1007_s13198-022-01769-0
    DOI: 10.1007/s13198-022-01769-0
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    References listed on IDEAS

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    1. Shuvashree Mondal & Debasis Kundu, 2019. "Point and Interval Estimation of Weibull Parameters Based on Joint Progressively Censored Data," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 81(1), pages 1-25, June.
    2. Shuvashree Mondal & Debasis Kundu, 2020. "On the joint Type-II progressive censoring scheme," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 49(4), pages 958-976, February.
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