Bayesian Prediction of Future Observations from Inverse Weibull Distribution Based on Type-II Hybrid Censored Sample

Abstract

In this paper, we have discussed the Bayesian procedure for the prediction of the future samples from inverse Weibull (IW) distribution under Type-II hybrid censoring scheme. Bayes estimators along with the corresponding highest posterior density (HPD) credible intervals have also been constructed for the parameters of IW distribution. The performance of the Bayes estimators of the model parameters has been compared with the maximum likelihood estimators through Monte Carlo Markov chain (MCMC) techniques. Finally, a real data set has been analyzed to illustrate the discussed methodology.