Estimation for Burr-X model based on progressively censored with random removals: Bayesian and non-Bayesian Approaches

Abstract: This paper considers the estimation problem for the Burr type-X, when the lifetimes are collected under Type-II progressive censoring with random removals, where the number of units removed at each failure time follows a binomial distribution. We use the methods of maximum likelihood as well as the Bayes procedure to derive both point and interval estimators of the parameters. The expected test time to complete the test is computed and analyzed for different censoring schemes. The effect of the binomial parameter p on the expected test time under progressive censoring and the relative expected test time over the complete sample are investigated. Monte Carlo simulations are performed to compare the performance of the different methods and for the expected termination time of the test. Furthermore, an example is presented for illustrative purposes.