期刊名称：Journal of Modern Applied Statistical Methods
出版年度：2016
卷号：15
期号：2
页码：21
出版社：Wayne State University
摘要：Finite mixture distributions consist of a weighted sum of standard distributions and are a useful tool for reliability analysis of a heterogeneous population. They provide the necessary flexibility to model failure distributions of components with multiple failure models. The analysis of the mixture models under Bayesian framework has received sizable attention in the recent years. However, the Bayesian estimation of the mixture models under doubly censored samples has not yet been introduced in the literature. The main objective of this paper is to discuss the Bayes estimation of the inverse Weibull mixture distributions under doubly censoring. Different priors and loss functions were assumed for the posterior estimation. The performance of the different estimators has been compared in terms of posterior risks.
