Bootstrap-Based T2 Multivariate Control Charts
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Phaladiganon, Poovich | - |
dc.contributor.author | Kim, Seoung Bum | - |
dc.contributor.author | Chen, Victoria C. P. | - |
dc.contributor.author | Baek, Jun-Geol | - |
dc.contributor.author | Park, Sun-Kyoung | - |
dc.date.accessioned | 2021-09-07T21:31:55Z | - |
dc.date.available | 2021-09-07T21:31:55Z | - |
dc.date.created | 2021-06-14 | - |
dc.date.issued | 2011 | - |
dc.identifier.issn | 0361-0918 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/114956 | - |
dc.description.abstract | Control charts have been used effectively for years to monitor processes and detect abnormal behaviors. However, most control charts require a specific distribution to establish their control limits. The bootstrap method is a nonparametric technique that does not rely on the assumption of a parametric distribution of the observed data. Although the bootstrap technique has been used to develop univariate control charts to monitor a single process, no effort has been made to integrate the effectiveness of the bootstrap technique with multivariate control charts. In the present study, we propose a bootstrap-based multivariate T2 control chart that can efficiently monitor a process when the distribution of observed data is nonnormal or unknown. A simulation study was conducted to evaluate the performance of the proposed control chart and compare it with a traditional Hotelling's T2 control chart and the kernel density estimation (KDE)-based T2 control chart. The results showed that the proposed chart performed better than the traditional T2 control chart and performed comparably with the KDE-based T2 control chart. Furthermore, we present a case study to demonstrate the applicability of the proposed control chart to real situations. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | TAYLOR & FRANCIS INC | - |
dc.subject | STATISTICAL PROCESS-CONTROL | - |
dc.title | Bootstrap-Based T2 Multivariate Control Charts | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Seoung Bum | - |
dc.contributor.affiliatedAuthor | Baek, Jun-Geol | - |
dc.identifier.doi | 10.1080/03610918.2010.549989 | - |
dc.identifier.scopusid | 2-s2.0-79952924760 | - |
dc.identifier.wosid | 000288677500002 | - |
dc.identifier.bibliographicCitation | COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, v.40, no.5, pp.645 - 662 | - |
dc.relation.isPartOf | COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION | - |
dc.citation.title | COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION | - |
dc.citation.volume | 40 | - |
dc.citation.number | 5 | - |
dc.citation.startPage | 645 | - |
dc.citation.endPage | 662 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Mathematics | - |
dc.relation.journalWebOfScienceCategory | Statistics & Probability | - |
dc.subject.keywordPlus | STATISTICAL PROCESS-CONTROL | - |
dc.subject.keywordAuthor | Average run length | - |
dc.subject.keywordAuthor | Bootstrap | - |
dc.subject.keywordAuthor | Hotelling&apos | - |
dc.subject.keywordAuthor | s T2 chart | - |
dc.subject.keywordAuthor | Kernel density estimation | - |
dc.subject.keywordAuthor | Multivariate control charts | - |
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