Bayesian inference for exponentiated  Pareto model with application to bladder cancer remission time

Sanjay Kumar  Singh; Umesh  Singh; Manoj  Kumar

doi:https://doi.org/10.59170/stattrans-2014-027

Bayesian inference for exponentiated Pareto model with application to bladder cancer remission time

Sanjay Kumar Singh Department of Statistics, Banaras Hindu University,Varanasi-221005 , Umesh Singh Department of Statistics and DST-CIMS, Banaras Hindu University,Varanasi-221005 , Manoj Kumar Assistant Prof. (Statistics), Department of School of Basic Science and Research, Sharda University, Greater Noida, UP. Statistics in Transition new series, vol. 15, 2014, 3, pages: 403-426 Published online: 1 September 2014 https://doi.org/10.59170/stattrans-2014-027

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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

KEYWORDS

PT-II CBR, MLE, bayes estimators, average experiment time

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