Bayesian inference for the parameters of Weibull distribution under progressive Type-I interval censored data with beta-binomial removals. Issue 4 (21st April 2017)

• Record Type: Journal Article
• Authors: Kaushik, Arun; Singh, Umesh; Singh, Sanjay Kumar
• Is Part Of: Communications in statistics. Volume 46:Issue 4(2017)
• Page Start: 3140
• Page End: 3158
• Publication Date: 2017-04-21
• Subjects: Beta Binomial Removals -- Clinical Trials -- Expected Total Number of Failures -- Progressive Type-I Interval Censoring -- Weibull Distribution; 62F10 -- 62F15 -- 62N01 -- 62N02 -- 62N05; Mathematical statistics -- Periodicals; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5
• DOI: 10.1080/03610918.2015.1076469
• Languages: English
• ISSNs: 0361-0918

Abstract

ABSTRACT: This article considers the problem of estimating the parameters of Weibull distribution under progressive Type-I interval censoring scheme with beta-binomial removals. Classical as well as the Bayesian procedures for the estimation of unknown model parameters have been developed. The Bayes estimators are obtained under SELF and GELF using MCMC technique. The performance of the estimators, has been discussed in terms of their MSEs. Further, expression for the expected number of total failures has been obtained. A real dataset of the survival times for patients with plasma cell myeloma is used to illustrate the suitability of the proposed methodology.