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    題名: Inference for the Weibull distribution with progressive hybrid censoring
    作者: Lin, Chien-tai;Chou, Cheng-chieh;Huang, Yen-lung
    貢獻者: 淡江大學數學學系
    關鍵詞: Gibbs sampling;Lindley’s approximation;Markov Chain Monte Carlo method;Metropolis–Hastings algorithm;Tierney–Kadane’s approximation
    日期: 2012-03-01
    上傳時間: 2012-05-24 11:05:31 (UTC+8)
    出版者: Amsterdam: Elsevier BV
    摘要: 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 Lindley (1980) and Tierney and Kadane (1986) 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.
    關聯: Computational Statistics and Data Analysis 56(3), pp.451-467
    DOI: 10.1016/j.csda.2011.09.002
    顯示於類別:[數學學系暨研究所] 期刊論文


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