Bayesian analysis of the Truncated Power Lindley under different loss functions for censored data

Abstract

"We perform a Bayesian analysis of the upper truncated power Lindley distribution based on type II censored data. Using various loss functions, including the generalized quadratic, entropy and Linex loss functions, we obtain Bayes estimators and their corresponding posterior risks. As tractable analytical forms of these estimators are out of reach, we propose Markov chain Monte-Carlo (MCMC) based simulation approach to study their performance. Moreover, given initial values for the parameters of the model, we obtain maximum likelihood estimators. Furthermore, we compare their performance with that of the Bayesian estimators using Pitman's closeness criterion and integrated mean square error. Finally, we illustrate our approach through an example with real data."