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    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/77036

    Title: Inference for the Weibull distribution with progressive hybrid censoring
    Authors: Lin, Chien-tai;Chou, Cheng-chieh;Huang, Yen-lung
    Contributors: 淡江大學數學學系
    Keywords: Gibbs sampling;Lindley’s approximation;Markov Chain Monte Carlo method;Metropolis–Hastings algorithm;Tierney–Kadane’s approximation
    Date: 2012-03-01
    Issue Date: 2012-05-24 11:05:31 (UTC+8)
    Publisher: Amsterdam: Elsevier BV
    Abstract: 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.
    Relation: Computational Statistics and Data Analysis 56(3), pp.451-467
    DOI: 10.1016/j.csda.2011.09.002
    Appears in Collections:[Graduate Institute & Department of Mathematics] Journal Article

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