Bayesian Estimation for GDUS Exponential Distribution Under Type-I Progressive Hybrid Censoring

This paper describes the classical and Bayesian inferences for the generalized DUS exponential distribution under type-I progressive hybrid censored data. In classical estimation; maximum likelihood estimator is used for obtaining estimates of the parameters. While in Bayesian context; two different losses namely squared error and linex loss function are used for estimation purpose. Metropolis–Hasting algorithm has applied to generate Markov chain Monte Carlo samples from the posterior density. In case of interval estimation; asymptotic confidence intervals and highest posterior density intervals for the unknown parameters are computed. The performance of estimators for different value of the parameters have done on the basis of mean square errors and risks. Lastly, a dataset is used to illustrate the proposed censoring methodology in a real-world situation.