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

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.

Keywords:

• Bayesian prediction
• Type-II hybrid censored sample
• constant-stress partially accelerated life test
• Weighted exponential distribution
• Markov Chain Monte Carlo technique

MATHEMATICS SUBJECT CLASSIFICATION:

• 62Fxx
• 62F15