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Bayesian prediction of future observations from weighted exponential distribution constant-stress model based on Type-II hybrid censored data

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  • Abd EL-Baset A. Ahmad
  • Mohamad A. Fawzy
  • Hosam Ouda

Abstract

In this paper, the problem of Bayesian prediction intervals for a future observations from weighted exponential distribution is concerned. Constant-stress partially accelerated life test under Type-II hybrid censoring scheme of the observed data is used. One- and two-sample Bayesian prediction intervals for a future observations based on Type-II hybrid censored data are derived. Markov Chain Monte Carlo (MCMC) technique is used to find Bayesian predictive intervals because one- and two-sample Bayesian predictive survival function cannot be obtained in closed-form. Finally, some numerical results are presented to illustrate all the inferential results developed in this paper.

Suggested Citation

  • Abd EL-Baset A. Ahmad & Mohamad A. Fawzy & Hosam Ouda, 2021. "Bayesian prediction of future observations from weighted exponential distribution constant-stress model based on Type-II hybrid censored data," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 50(12), pages 2732-2746, June.
    • Handle: RePEc:taf:lstaxx:v:50:y:2021:i:12:p:2732-2746
    DOI: 10.1080/03610926.2019.1667394
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