Inference for the Weibull distribution with progressive hybrid censoring

機構典藏 > College of Sciences > Graduate Institute & Department of Mathematics > Journal Article > Item 987654321/77036

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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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