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Bayesian Reliability Estimation In Extension of Exponential Distribution for Progressive Type II Censored Data with Binomial Removals Using Different Loss Functions

S. K. Singh, U. Singh, Abhimanyu Singh Yadav

Abstract


The present study deals with the estimation problem for the parameter estimation and reliability function of the extension of exponential distribution under progressive Type II censoring scheme with Binomial removals using different loss functions. We use the classical and Bayesian procedures to obtain the estimates of the parameters and reliability function of extension of exponential distribution. We have also constructed the 95 % asymptotic and highest posterior density (HPD)
intervals for the parameters. Further, Monte Carlo simulation technique has been used to compare both the methods used with different random schemes in terms of their simulated risk.

Keywords


Maximum likelihood estimator, Bayes estimator, Reliability function, Different loss function, MCMC method.

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