Bayesian inference: Weibull poisson model for censored data using the expectation–maximization algorithm and its application to bladder cancer data
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Date
2022
Journal Title
Journal ISSN
Volume Title
Publisher
Journal of Applied Statistics
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.
Description
Keywords
PT-II CBRs; expectation–maximization algorithm; GELF; Bayes prediction; expected experiment time; likelihood ratio test 1.