Inference from two-variable degradation data using genetic algorithm and Markov chain Monte Carlo methods
學年 107
學期 1
出版（發表）日期 2018-09-01
作品名稱 Inference from two-variable degradation data using genetic algorithm and Markov chain Monte Carlo methods
作品名稱（其他語言）
著者 Jyun-You Chiang; Jianping Zhu; Yu-Jau Lin; Y. L. Lio; Tzong-Ru Tsai
單位
出版者
著錄名稱、卷期、頁數 International Journal of Information and Management Sciences 29(3), p.235-256
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.
關鍵字 Cumulative exposure model;Gibbs sampling algorithm;Markov chain Monte Carlo;Metropolis-Hastings algorithm
語言 en_US
ISSN
期刊性質 國外
收錄於 EI Scopus
產學合作
通訊作者
審稿制度 是
國別 TWN
公開徵稿
出版型式 ,電子版
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