Classical and Bayesian Inference for an Extension of the Exponential Distribution under Progressive Type-II Censored Data with Binomial Removals

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Maximum likelihood and Bayes estimators of the unknown parameters of an extension of the exponential (EE) distribution have been obtained for Progressive Type-II Censored data with Binomial removals. Markov Chain Monte Carlo (MCMC) method is used to compute the Bayes estimates of the parameters of interest. The General Entropy Loss Function (GELF) and Squared Error Loss Function (SELF) have been considered for obtaining the Bayes estimators. Comparisons are made between Bayesian and Maximum likelihood estimators (MLEs) via Monte Carlo simulation. An example is discussed to illustrate its applicability.