Statistical Inference for the Weighted Exponential Distribution under Progressive Type-II Censoring with Binomial Removal

SYNOPTIC ABSTRACT: In this article, we consider estimation of the unknown parameters of a weighted exponential distribution under Type II progressive censoring with binomial removals, where the number of units removed at each failure time follows a binomial distribution. Maximum likelihood and Bayes procedure are used to derive both point and interval estimates of the parameters involved in the model. The expected termination point to complete the censoring test is computed and analyzed under binomial censoring scheme. Further, the discussion has been extended to one- and two-sample prediction estimates and intervals of the future samples under Bayesian paradigm. A numerical study is conducted to compare the performance of the procedures by means of Monte Carlo simulations. Finally, real life examples are analyzed for illustrative purposes. © 2018 Taylor & Francis Group, LLC.