On estimation of two-component mixture inverse Lomax model via Bayesian approach

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• 作者：Jafer Rahman ; Muhammad Aslam
• 关键词：Inverse Lomax distribution ; Mixture model ; Type ; I censoring ; Bayes estimators and Bayes risks ; ML estimators and variances ; Loss functions
• 刊名：International Journal of System Assurance Engineering and Management
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：8
• 期：1-supp
• 页码：99-109
• 全文大小：
• 刊物类别：Engineering
• 刊物主题：Quality Control, Reliability, Safety and Risk; Engineering Economics, Organization, Logistics, Marketing;
• 出版者：Springer India
• ISSN：0976-4348
• 卷排序：8

文摘

A mixture distribution is the probability distribution of observations in the pooled population which is used to make statistical inferences about the characteristics of the sub-populations on the basis of sample data from the joint population. This article comprises such sort of study for unknown parameters of two-component mixture inverse Lomax distribution based on Bayesian thoughts. Bayes estimators and Bayes posterior risks for the parameters are derived under various loss functions along with the use of conjugate priors. Numerical results for Bayes estimates and Bayes risks are obtained by simulation as well as real data. The study also includes Maximum likelihood estimation for the comparisons with Bayesian estimation.