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dc.contributor.author배석주-
dc.date.accessioned2019-02-20T01:07:58Z-
dc.date.available2019-02-20T01:07:58Z-
dc.date.issued2016-10-
dc.identifier.citationAPPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY, v. 32, issue 5, Page. 660-676en_US
dc.identifier.issn1524-1904-
dc.identifier.issn1526-4025-
dc.identifier.urihttps://onlinelibrary.wiley.com/doi/full/10.1002/asmb.2186-
dc.identifier.urihttps://repository.hanyang.ac.kr/handle/20.500.11754/99082-
dc.description.abstractThe purpose of this article is to summarize recent research results for constructing nonparametric multivariate control charts with main focus on data depth-based control charts. Data depth provides dimension reduction to high-dimensional problems in a completely nonparametric way. Several depth measures including Tukey depth are shown to be particularly effective for purposes of statistical process control in case that the data deviate normality assumption. For detecting small or moderate shifts in the process target mean, the multivariate version of the exponentially weighted moving average chart is generally robust to non-normal data, so that nonparametric alternatives may be less often required. Copyright (c) 2016 John Wiley Sons, Ltd.en_US
dc.description.sponsorshipWe thank the referees who helped us gain substantial improvements in the manuscript. This work was supported by Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education (No. 201500000002571), and Leading Core Technology Program sponsored by Defense Acquisition Program Administration and Agency for Defense Development under the project title "High-Performance PMD Technology for Guided Missiles".en_US
dc.language.isoenen_US
dc.publisherWILEY-BLACKWELLen_US
dc.subjectdata depthen_US
dc.subjectHotelling T-2 statisticen_US
dc.subjectMahalanobis distanceen_US
dc.subjectShewhart charten_US
dc.subjectTukey depthen_US
dc.titleOn data depth and the application of nonparametric multivariate statistical process control chartsen_US
dc.typeArticleen_US
dc.relation.volume32-
dc.identifier.doi10.1002/asmb.2186-
dc.relation.page660-676-
dc.relation.journalAPPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY-
dc.contributor.googleauthorBae, Suk Joo-
dc.contributor.googleauthorDo, Giang-
dc.contributor.googleauthorKvam, Paul-
dc.relation.code2016004275-
dc.sector.campusS-
dc.sector.daehakCOLLEGE OF ENGINEERING[S]-
dc.sector.departmentDEPARTMENT OF INDUSTRIAL ENGINEERING-
dc.identifier.pidsjbae-
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COLLEGE OF ENGINEERING[S](공과대학) > INDUSTRIAL ENGINEERING(산업공학과) > Articles
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