Bayesian and Non-Bayesian Estimation of Composed Inverted Generalized Exponential – Exponential Distribution

In this paper, we introduce the composed- inverted generalized exponential- exponential (C-IGEE) distribution. The point and interval estimations based on maximum likelihood are proposed. We also obtain the Bayes estimates of the unknown parameters under the assumption of independent gamma priors. The Bayes estimates of the unknown parameters cannot be obtained in a closed form. So, Markov Chain Monte Carlo (MCMC) method has been used to compute the approximate Bayes estimates under the squared error loss function and also construct the highest posterior density (HPD) intervals. Further, a simulation study has been conducted to compare the performances of Bayes estimators with corresponding maximum likelihood estimators