Bayesian inference: Weibull Poisson model for censored data using the expectation–maximization algorithm and its application to bladder cancer data. Issue 4 (12th March 2022)

• Record Type: Journal Article
• Authors: Pathak, Anurag; Kumar, Manoj; Singh, Sanjay Kumar; Singh, Umesh
• Is Part Of: Journal of applied statistics. Volume 49:Issue 4(2022)
• Page Start: 926
• Page End: 948
• Publication Date: 2022-03-12
• Subjects: PT-II CBRs -- expectation–maximization algorithm -- GELF -- Bayes prediction -- expected experiment time -- likelihood ratio test; Statistics -- Periodicals; 519.5
• DOI: 10.1080/02664763.2020.1845626
• Languages: English
• ISSNs: 0266-4763

Abstract

Abstract : This article focuses on the parameter estimation of experimental items/units from Weibull Poisson Model under progressive type-II censoring with binomial removals (PT-II CBRs). The expectation–maximization algorithm has been used for maximum likelihood estimators (MLEs). The MLEs and Bayes estimators have been obtained under symmetric and asymmetric loss functions. Performance of competitive estimators have been studied through their simulated risks. One sample Bayes prediction and expected experiment time have also been studied. Furthermore, through real bladder cancer data set, suitability of considered model and proposed methodology have been illustrated.