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Comparison of Bayesian and Classical Methods for Analysis of Left Censored Burr Type III Distribution

N. Feroze, M. Aslam


The paper addresses the problem of estimation of parameters of the Burr type III distribution based on Bayesian and maximum likelihood estimation (MLE) when the samples are left censored. As the closed form expression for the MLEs and Bayes estimators of the parameters along with their variances cannot be derived, the approximate solutions have been obtained through iterative procedures. A class of priors and loss functions has been assumed for posterior analysis. An extensive simulation study has been carried out to compare the performance of different estimators. The study revealed that performance of Bayesian estimation under informative priors is better than MLE. In addition, the Gumbel type ii prior has been discovered to be the most efficient prior.


maximum likelihood estimation, loss functions, prior distribution, Bayes risks

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