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BAYESIAN ESTIMATION BASED ON FIRST FAILURE CENSORED DATA

In this paper, we discuss the problem of estimating the parameters of the generalized linear exponential distribution based on progressive firstfailure censoring scheme. The maximum likelihood and Bayes methods of estimation are used. The Markov Chain Monte Carlo technique is used for computing the Bayes estimates under informative and non-informative priors. The Bayes estimates of the parameters are compared with their corresponding maximum likelihood estimates. A numerical example is provided to illustrate the proposed methods. A real data sets are used to show the performance of the censoring schemes using maximum likelihood estimator and Bayes estimator.