Bayesian Prediction for Unobserved Data from Exponentiated Weibull Distribution based on Progressive Type II Censoring
نویسندگان
چکیده
In this paper, we consider Bayesian estimation and prediction problem for the parameters and unobserved lifetimes of exponentiated Weibull distribution based on a progressively type II censored samples. By using an extended likelihood function and informative joint prior distributions, the joint posterior density for the parameters and unobserved lifetimes of units censored at the failure time is obtained. Since the closed form of Bayes estimators does not exist, we use Markov Chain Monte Carlo (MCMC) method such as Gibbs sampling and MetropolisHastings algorithm to generate the posterior conditional probabilities of interest. Monte Carlo simulations and real data analysis are conducted to observe the behaviour of the proposed method.
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