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Gower distance-based multivariate control charts for a mixture of continuous and categorical variables

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dc.contributor.authorTuerhong, Gulanbaier-
dc.contributor.authorKim, Seoung Bum-
dc.date.accessioned2021-09-05T10:51:38Z-
dc.date.available2021-09-05T10:51:38Z-
dc.date.created2021-06-15-
dc.date.issued2014-03-
dc.identifier.issn0957-4174-
dc.identifier.urihttps://scholar.korea.ac.kr/handle/2021.sw.korea/99108-
dc.description.abstractProcesses characterized by high dimensional and mixture data challenge traditional statistical process control charts. In this study, we propose a multivariate control chart based on the Gower distance that can handle a mixture of continuous and categorical data. An extensive simulation study was conducted to examine the properties of the proposed control chart under various scenarios and compared it with some existing multivariate control charts. The simulation results revealed that the proposed control chart outperformed the existing charts when the number of categorical variables increases. Furthermore, we demonstrated the applicability and effectiveness of the proposed control charts through a real case study. (C) 2013 Elsevier Ltd. All rights reserved.-
dc.languageEnglish-
dc.language.isoen-
dc.publisherPERGAMON-ELSEVIER SCIENCE LTD-
dc.subjectSTATISTICAL PROCESS-CONTROL-
dc.subjectNEAREST NEIGHBOR RULE-
dc.subjectEWMA CONTROL CHART-
dc.subjectFAULT-DETECTION-
dc.subjectMANUFACTURING PROCESSES-
dc.subjectARTIFICIAL CONTRASTS-
dc.subjectSIGNALS-
dc.subjectMODEL-
dc.titleGower distance-based multivariate control charts for a mixture of continuous and categorical variables-
dc.typeArticle-
dc.contributor.affiliatedAuthorKim, Seoung Bum-
dc.identifier.doi10.1016/j.eswa.2013.08.068-
dc.identifier.scopusid2-s2.0-84888379498-
dc.identifier.wosid000329955900018-
dc.identifier.bibliographicCitationEXPERT SYSTEMS WITH APPLICATIONS, v.41, no.4, pp.1701 - 1707-
dc.relation.isPartOfEXPERT SYSTEMS WITH APPLICATIONS-
dc.citation.titleEXPERT SYSTEMS WITH APPLICATIONS-
dc.citation.volume41-
dc.citation.number4-
dc.citation.startPage1701-
dc.citation.endPage1707-
dc.type.rimsART-
dc.type.docTypeArticle-
dc.description.journalClass1-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaComputer Science-
dc.relation.journalResearchAreaEngineering-
dc.relation.journalResearchAreaOperations Research & Management Science-
dc.relation.journalWebOfScienceCategoryComputer Science, Artificial Intelligence-
dc.relation.journalWebOfScienceCategoryEngineering, Electrical & Electronic-
dc.relation.journalWebOfScienceCategoryOperations Research & Management Science-
dc.subject.keywordPlusSTATISTICAL PROCESS-CONTROL-
dc.subject.keywordPlusNEAREST NEIGHBOR RULE-
dc.subject.keywordPlusEWMA CONTROL CHART-
dc.subject.keywordPlusFAULT-DETECTION-
dc.subject.keywordPlusMANUFACTURING PROCESSES-
dc.subject.keywordPlusARTIFICIAL CONTRASTS-
dc.subject.keywordPlusSIGNALS-
dc.subject.keywordPlusMODEL-
dc.subject.keywordAuthorGower distance-
dc.subject.keywordAuthorMultivariate control charts-
dc.subject.keywordAuthorMixture data-
dc.subject.keywordAuthorQuality control-
dc.subject.keywordAuthorStatistical process control-
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