Inference for the Weibull distribution with progressive hybrid censoring

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• 作者：Chien-Tai Lin  ; ;   ; ^a ;   ; ^{chien@mail.tku.edu.tw} ; Cheng-Chieh Chou^a ; Yen-Lung Huang^a
• 关键词：Gibbs sampling ; Lindley&rsquo ; s approximation ; Markov Chain Monte Carlo method ; Metropolis&ndash ; Hastings algorithm ; Tierney&ndash ; Kadane&rsquo ; s approximation
• 刊名：Computational Statistics and Data Analysis
• 出版年：2012
• 出版时间：1 March 2012
• 年：2012
• 卷：56
• 期：3
• 页码：451-467
• 全文大小：483 K

文摘

Recently, progressive hybrid censoring schemes have become quite popular in life-testing and reliability studies. In this paper, we investigate the maximum likelihood estimation and Bayesian estimation for a two-parameter Weibull distribution based on adaptive Type-I progressively hybrid censored data. The Bayes estimates of the unknown parameters are obtained by using the approximation forms of and as well as two Markov Chain Monte Carlo methods under the assumption of gamma priors. Computational formulae for the expected number of failures is provided and it can be used to determine the optimal adaptive Type-I progressive hybrid censoring schemes under a pre-determined budget of experiment.