Bayesian inference: Weibull Poisson model for censored data using the expectation–maximization algorithm and its application to bladder cancer data |
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Authors: | Anurag Pathak Manoj Kumar Sanjay Kumar Singh Umesh Singh |
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Institution: | aDepartment of Statistics, Central University of Haryana, Mahendragarh, India;bDepartment of Statistics, Banaras Hindu University, Varanasi, India |
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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. |
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Keywords: | PT-II CBRs expectation– maximization algorithm GELF Bayes prediction expected experiment time likelihood ratio test |
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