Gower distance-based multivariate control charts for a mixture of continuous and categorical variables
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Tuerhong, Gulanbaier | - |
dc.contributor.author | Kim, Seoung Bum | - |
dc.date.accessioned | 2021-09-05T10:51:38Z | - |
dc.date.available | 2021-09-05T10:51:38Z | - |
dc.date.created | 2021-06-15 | - |
dc.date.issued | 2014-03 | - |
dc.identifier.issn | 0957-4174 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/99108 | - |
dc.description.abstract | Processes 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.language | English | - |
dc.language.iso | en | - |
dc.publisher | PERGAMON-ELSEVIER SCIENCE LTD | - |
dc.subject | STATISTICAL PROCESS-CONTROL | - |
dc.subject | NEAREST NEIGHBOR RULE | - |
dc.subject | EWMA CONTROL CHART | - |
dc.subject | FAULT-DETECTION | - |
dc.subject | MANUFACTURING PROCESSES | - |
dc.subject | ARTIFICIAL CONTRASTS | - |
dc.subject | SIGNALS | - |
dc.subject | MODEL | - |
dc.title | Gower distance-based multivariate control charts for a mixture of continuous and categorical variables | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Seoung Bum | - |
dc.identifier.doi | 10.1016/j.eswa.2013.08.068 | - |
dc.identifier.scopusid | 2-s2.0-84888379498 | - |
dc.identifier.wosid | 000329955900018 | - |
dc.identifier.bibliographicCitation | EXPERT SYSTEMS WITH APPLICATIONS, v.41, no.4, pp.1701 - 1707 | - |
dc.relation.isPartOf | EXPERT SYSTEMS WITH APPLICATIONS | - |
dc.citation.title | EXPERT SYSTEMS WITH APPLICATIONS | - |
dc.citation.volume | 41 | - |
dc.citation.number | 4 | - |
dc.citation.startPage | 1701 | - |
dc.citation.endPage | 1707 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Operations Research & Management Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Operations Research & Management Science | - |
dc.subject.keywordPlus | STATISTICAL PROCESS-CONTROL | - |
dc.subject.keywordPlus | NEAREST NEIGHBOR RULE | - |
dc.subject.keywordPlus | EWMA CONTROL CHART | - |
dc.subject.keywordPlus | FAULT-DETECTION | - |
dc.subject.keywordPlus | MANUFACTURING PROCESSES | - |
dc.subject.keywordPlus | ARTIFICIAL CONTRASTS | - |
dc.subject.keywordPlus | SIGNALS | - |
dc.subject.keywordPlus | MODEL | - |
dc.subject.keywordAuthor | Gower distance | - |
dc.subject.keywordAuthor | Multivariate control charts | - |
dc.subject.keywordAuthor | Mixture data | - |
dc.subject.keywordAuthor | Quality control | - |
dc.subject.keywordAuthor | Statistical process control | - |
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