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Estimation of Parameters of Exponentiated Pareto Model for Progressive Type-II Censored Data with Binomial Removals Using Markov Chain Monte Carlo Method

Sanjay Kumar Singh, Umesh Singh, Manoj Kumar


Maximum likelihood and Bayes estimators of the unknown parameters and the expected experiment times of the exponentiated Pareto distribution have been obtained for progressive type-ii censored data with binomial removal scheme. 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 via Monte carlo simulation.


Maximum likelihood, Exponentiated Pareto distribution.

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