Burr-X Model Estimate using Bayesian and non-Bayesian Approaches

Abstract

The present paper is aimed at developing Bayesian and Maximum Likelihood estimations (ML) of the Burr type-X model of distribution when data are gathered from Type-II cumulative censoring with binomial eliminations. The procedures for getting the (ML) evaluations of the parameters are examined. The Bayes technique to get both point and interval estimators of the parameters are illustrated. The expected termination time for Type-II cumulative censoring with binomial eliminations is analyzed after carrying out the computation. Classical and Bayes procedures are improved in the case of parameter estimation and evaluated the expected test time for Burr-X model under cumulative censoring wit binomial sweep. A simulation study is performed to compare the implementation of the various procedures and for the expected termination time of the test. Finally, illustrative examples are given and the results from emulation studies determining the achievement of the suggested techniques are presented.