出版日期：2018-09-01 00:00:00

著者：Jyun-YouChiang; Y.L. Lio; H.K.T.Ng; Tzong-RuTsai; Ting Li

著錄名稱、卷期、頁數：Computers and Industrial Engineering 123, p.119-133

摘要：This paper presents two model selection approaches, namely the random data-driven approach and the weighted modeling approach, to construct robust bootstrap control charts for process monitoring of percentiles of the shape-scale class of distributions under model uncertainty. The generalized exponential, lognormal and Weibull distributions are considered as candidate distributions to establish the proposed process control procedures. Monte Carlo simulations are conducted with various combinations of the percentiles, false-alarm rates and sample sizes to evaluate the performance of the proposed robust bootstrap control charts in terms of the average run lengths. Simulation results exhibit that the two proposed robust model selection approaches perform well when the underlying distribution of the quality characteristic is unknown. Finally, the proposed process monitoring procedures are applied to two data sets for illustration.

關鍵字：Bootstrap control chart;Maximum likelihood estimate;Model discrimination;Percentiles;Shape-scale distribution

語言：en_US

期刊性質：國外

收錄於：SCI Scopus NotTSSCI

通訊作者：J-Y Chiang

審稿制度：是

國別：NLD

出版型式：電子版