A class of improved informative priors for bayesian analysis of two-component mixture of failure time distributions from doubly censored data

Abstract

The Bayesian analysis of the mixture models has received a sizable attention of the analysts during recent years. However, most of the contributions have been discussed under singly type I censored samples. This paper aims to discuss the Bayesian analysis of the two-component mixture of lifetime distribution, with a particular case for Rayleigh distribution, under doubly censored samples. A class of improved informative priors has been assumed for posterior estimation. The squared error and k-loss functions have been proposed to derive the Bayes estimators and the corresponding posterior risks. The prior elicitation has been discussed via prior predictive approach. The comparisons among the performance of different estimators have been made in terms of posterior risks based on analysis of simulated and real life data sets.

Keywords:

• Inverse Transformation Method
• Mixture Model
• Doubly Censoring
• Loss Functions
• Bayes Estimator