Comparison of the Bayesian Methods on Interval-Censored Data for Weibull Distribution  

作　　者：Al Omari Mohammed Ahmed 

机构地区：[1]Department of Mathematics, Faculty of Arts and Science in Qilwah, Al Baha University, Baha, KSA

出　　处：《Open Journal of Statistics》2014年第8期570-577,共8页统计学期刊（英文）

摘　　要：This study considers the estimation of Maximum Likelihood Estimator and the Bayesian Estimator of the Weibull distribution with interval-censored data. The Bayesian estimation can’t be used to solve the parameters analytically and therefore Markov Chain Monte Carlo is used, where the full conditional distribution for the scale and shape parameters are obtained via Metropolis-Hastings algorithm. Also Lindley’s approximation is used. The two methods are compared to maximum likelihood counterparts and the comparisons are made with respect to the mean square error (MSE) to determine the best for estimating of the scale and shape parameters.This study considers the estimation of Maximum Likelihood Estimator and the Bayesian Estimator of the Weibull distribution with interval-censored data. The Bayesian estimation can’t be used to solve the parameters analytically and therefore Markov Chain Monte Carlo is used, where the full conditional distribution for the scale and shape parameters are obtained via Metropolis-Hastings algorithm. Also Lindley’s approximation is used. The two methods are compared to maximum likelihood counterparts and the comparisons are made with respect to the mean square error (MSE) to determine the best for estimating of the scale and shape parameters.

关 键 词：Weibull DISTRIBUTION BAYESIAN Method INTERVAL Censored METROPOLIS-HASTINGS Algorithm Lindley’s APPROXIMATION 

分 类 号：O21[理学—概率论与数理统计]