Bayesian Estimation of Generalized Exponential Distribution under Progressive First Failure Censored Sample
نویسندگان
چکیده
In this paper, we consider the maximum likelihood (ML) and Bayes estimation of the parameters of the generalized exponential distribution based on progressive first failure censored samples. We also consider the problem of predicting an independent future order statistics from the same distribution. However, since Bayes estimator do not exist in an explicit form for the parameters, Markov Chain Monte Carlo (MCMC) method is used to generate samples from the posterior distribution. Importance sampling is applied to estimate the parameters and to predict the future observations. Simulation data are analyzed for illustrative purpose.
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