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Generalized Bayes Estimation Based on a Joint Type-II Censored Sample from K-Exponential Populations

Author

Listed:
  • Yahia Abdel-Aty

    (Department of Mathematics, College of Science, Taibah University, Al-Madinah Al-Munawarah 30002, Saudi Arabia
    Department of Mathematics, Faculty of Science, Al-Azhar University, Nasr City 11884, Egypt)

  • Mohamed Kayid

    (Department of Statistics and Operations Research, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia)

  • Ghadah Alomani

    (Department of Mathematical Sciences, College of Science, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia)

Abstract

Generalized Bayes is a Bayesian study based on a learning rate parameter. This paper considers a generalized Bayes estimation to study the effect of the learning rate parameter on the estimation results based on a joint censored sample of type-II exponential populations. Squared error, Linex, and general entropy loss functions are used in the Bayesian approach. Monte Carlo simulations were performed to assess how well the different approaches perform. The simulation study compares the Bayesian estimators for different values of the learning rate parameter and different losses.

Suggested Citation

  • Yahia Abdel-Aty & Mohamed Kayid & Ghadah Alomani, 2023. "Generalized Bayes Estimation Based on a Joint Type-II Censored Sample from K-Exponential Populations," Mathematics, MDPI, vol. 11(9), pages 1-11, May.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:9:p:2190-:d:1140570
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    References listed on IDEAS

    as
    1. Yahia Abdel-Aty, 2017. "Exact likelihood inference for two populations from two-parameter exponential distributions under joint Type-II censoring," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(18), pages 9026-9041, September.
    2. Parsi, Safar & Bairamov, Ismihan, 2009. "Expected values of the number of failures for two populations under joint Type-II progressive censoring," Computational Statistics & Data Analysis, Elsevier, vol. 53(10), pages 3560-3570, August.
    3. P. G. Bissiri & C. C. Holmes & S. G. Walker, 2016. "A general framework for updating belief distributions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(5), pages 1103-1130, November.
    4. Ryan Martin & Bo Ning, 2020. "Empirical Priors and Coverage of Posterior Credible Sets in a Sparse Normal Mean Model," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 82(2), pages 477-498, August.
    5. Jeffrey W. Miller & David B. Dunson, 2019. "Robust Bayesian Inference via Coarsening," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(527), pages 1113-1125, July.
    6. Balakrishnan, N. & Rasouli, Abbas, 2008. "Exact likelihood inference for two exponential populations under joint Type-II censoring," Computational Statistics & Data Analysis, Elsevier, vol. 52(5), pages 2725-2738, January.
    7. S P Lyddon & C C Holmes & S G Walker, 2019. "General Bayesian updating and the loss-likelihood bootstrap," Biometrika, Biometrika Trust, vol. 106(2), pages 465-478.
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