Robust bootstrap control charts for percentiles based on model selection approaches
學年 107
學期 1
出版（發表）日期 2018-09-01
作品名稱 Robust bootstrap control charts for percentiles based on model selection approaches
作品名稱（其他語言）
著者 Jyun-YouChiang; Y.L. Lio; H.K.T.Ng; Tzong-RuTsai; Ting Li
單位
出版者
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
ISSN
期刊性質 國外
收錄於 SCI Scopus NotTSSCI
產學合作
通訊作者 J-Y Chiang
審稿制度 是
國別 NLD
公開徵稿
出版型式 ,電子版
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/114966 )

SDGS 優質教育,產業創新與基礎設施