Bayesian Inference for Exponentiated Pareto Model with Application to Bladder Cancer Remission Time
Bayesian Inference for Exponentiated Pareto Model with Application to Bladder Cancer Remission Time

Summary/Abstract: Maximum likelihood and Bayes estimators of the unknown parameters and the expected experiment times of the exponentiated Pareto model 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 generalized entropy loss function and squared error loss function have been considered for obtaining the Bayes estimators. Comparisons are made between Bayesian and maximum likelihood (ML) estimators via Monte Carlo simulation. The proposed methodology is illustrated through real data.

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