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    题名: Inference from two-variable degradation data using genetic algorithm and Markov chain Monte Carlo methods
    作者: Jyun-You Chiang;Jianping Zhu;Yu-Jau Lin;Y. L. Lio;Tzong-Ru Tsai
    关键词: Cumulative exposure model;Gibbs sampling algorithm;Markov chain Monte Carlo;Metropolis-Hastings algorithm
    日期: 2018-09
    上传时间: 2018-12-20 12:10:18 (UTC+8)
    出版者: Tamakng University Department of Management Sciences
    摘要: Two-variable gamma process with generalized Eyring model have been widely used to assess the reliability of reliable products in engineering applications. Because no close forms of the maximum likelihood estimators for the model parameters can be derived and iterative procedure to evaluate the maximum likelihood estimate is very sensitive to the initial input and difficult to control, the analytic genetic algorithm, Gibbs sampling Markov chain Monte Carlo algorithm and Metropolis-Hastings Markov chain Monte Carlo algorithm methods are established and applied to implementing parameter estimation for the gamma process. The performance of those methods are evaluated through simulations. Simulation results show that the Markov chain Monte Carlo method based maximum likelihood estimates outperform the other competitors with smaller bias and mean squared error. The application of the proposed methods was illustrated with a lumen degradation data set of light-emitting diodes.
    關聯: International Journal of Information and Management Sciences 29(3), p.235-256
    DOI: 10.6186%2fIJIMS.201809_29(3).0001
    显示于类别:[統計學系暨研究所] 期刊論文


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