Bayesian inference: Weibull poisson model for censored data using the expectation–maximization algorithm and its application to bladder cancer data

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dc.contributor.authorAnurag, Pathak
dc.contributor.authorKumar, Manoj
dc.contributor.authorSingh, Sanjay Kumar
dc.contributor.authorSingh, Umesh
dc.date.accessioned2023-04-25T10:56:25Z
dc.date.available2023-04-25T10:56:25Z
dc.date.issued2022
dc.description.abstractThis 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.en_US
dc.identifier.urihttp://hdl.handle.net/123456789/979
dc.language.isoenen_US
dc.publisherJournal of Applied Statisticsen_US
dc.subjectPT-II CBRs; expectation–maximization algorithm; GELF; Bayes prediction; expected experiment time; likelihood ratio test 1.en_US
dc.titleBayesian inference: Weibull poisson model for censored data using the expectation–maximization algorithm and its application to bladder cancer dataen_US
dc.typeArticleen_US
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