Different Methods of Analysis of Progressive Type-II Censoring from Burr Type- XII Model in Presence Competing Risk Data
Keywords:Burr type XII distribution, Progressive type-II censoring, Competing risks, Gibbs and Metropolis-Hasting samplers, credible intervals, Bootstrap
In this paper we will study the maximum likelihood estimation (MLE) for the unknown parameters of the Burr Type-XII model when the data is progressively type-II censored in presence competing risk data, On the basis of this type of censoring, we will use the classical methods and the Bayesian method. We will compare the classic methods, which include the ML and parametric bootstrap methods with the Bayes method under squared error loss (SEL) function, we propose to apply Markov chain Monte Carlo (MCMC) technique to carry out a Bayesian estimation procedure as will as to construct the credible intervals. An example of real data is provided for illustration. Finally, all available methods were compared using Monte Carlo simulation.
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