BAYESIAN INFERENCE OF GENERALIZED EXPONENTIAL DISTRIBUTION BASED ON LOWER RECORD VALUES

摘要

This article addresses the problem of frequentist and Bayesian estimation of the parameters of the generalized exponential distribution (GED) using lower record values. The maximum likelihood estimates (MLE) and the Bayes estimates based on lower records are derived for the unknown parameters of the GED. We consider the Bayes estimators of the unknown parameters under the assumption of gamma priors on both the shape and the scale parameters. The Bayes estimators cannot be obtained in explicit forms. The Bayesian estimation of the parameters of the GED has been studied with respect to both symmetric and asymmetric loss functions. We have also derived the Bayes interval of this distribution and discussed the Bayesian prediction intervals of the future record values based on the observed record values. Monte Carlo simulations are performed to compare the performances of the proposed methods, and one dataset has been analyzed for illustrative purposes.