首页 | 本学科首页   官方微博 | 高级检索  
     


Bayesian inference for the randomly censored Weibull distribution
Abstract:In this paper, we consider the Bayesian inference of the unknown parameters of the randomly censored Weibull distribution. A joint conjugate prior on the model parameters does not exist; we assume that the parameters have independent gamma priors. Since closed-form expressions for the Bayes estimators cannot be obtained, we use Lindley's approximation, importance sampling and Gibbs sampling techniques to obtain the approximate Bayes estimates and the corresponding credible intervals. A simulation study is performed to observe the behaviour of the proposed estimators. A real data analysis is presented for illustrative purposes.
Keywords:random censoring  squared error loss function  prior distribution  Bayes estimates  importance sampling  Gibbs sampling  Lindley's approximation  Markov chain Monte Carlo
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号