BAYESIAN ANALYSIS UNDER UNBALANCED AND BALANCED LOSS FUNCTIONS APPLYING DIFFERENT PRIOR INFORMATIONS

Abstract

In this paper, We perform a Bayesian analysis of Zeghdoudi distribution based on type II censored data. Using two type of loss functions; balanced and unbalanced loss functions, we use three different loss functions. this estimation includes three cases of prior informations; availability and lack of primary information, we obtain Bayes estimators and the corresponding posterior risks. the analytical forms of these estimators are out of reach, so, we propose Markov chain Monte-Carlo (MCMC) procedure. Moreover, given initial values for the parameters of the model,we obtain maximum likelihood estimators. Furthermore, we compare their performance with those of the Bayesian estimators using balanced and unbalanced loss functions.