Bayesian multiple change-point estimation for exponential distribution with truncated and censored data. Issue 12 (18th June 2017)

• Record Type: Journal Article
• Authors: He, Chaobing
• Is Part Of: Communications in statistics. Volume 46:Issue 12(2017)
• Page Start: 5827
• Page End: 5839
• Publication Date: 2017-06-18
• Subjects: Complete-data likelihood function -- full conditional distribution -- Gibbs sampling -- MCMC methods -- rejection sampling method -- truncated and censored data.; 62F15; Mathematical statistics -- Periodicals; Mathematics; Statistics; 519.2
• DOI: 10.1080/03610926.2016.1161797
• Languages: English
• ISSNs: 0361-0926

Abstract

ABSTRACT: This paper considers the multiple change-point estimation for exponential distribution with truncated and censored data by Gibbs sampling. After all the missing data of interest is filled in by some sampling methods such as rejection sampling method, the complete-data likelihood function is obtained. The full conditional distributions of all parameters are discussed. The means of Gibbs samples are taken as Bayesian estimations of the parameters. The implementation steps of Gibbs sampling are introduced in detail. Finally random simulation test is developed, and the results show that Bayesian estimations are fairly accurate.