Comparison of Novelty Score- Based Multivariate Control Charts
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Tuerhong, Gulanbaier | - |
dc.contributor.author | Kim, Seoung Bum | - |
dc.date.accessioned | 2021-09-04T15:59:05Z | - |
dc.date.available | 2021-09-04T15:59:05Z | - |
dc.date.created | 2021-06-18 | - |
dc.date.issued | 2015-05-28 | - |
dc.identifier.issn | 0361-0918 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/93515 | - |
dc.description.abstract | Control charts are widely used in various industries to improve product quality. One recent trend in developing control charts is based on novelty score algorithms that can effectively describe reality and reflect the unique characteristics of the data being monitored. In this study, we compared eight novelty score algorithmsthe T-2, Local T-2, D-max, D-mean, K-2, the k-nearest neighbor data description, the local density outlier factor, and the hybrid novelty score (HNS)in terms of their average run length performance. A rigorous simulation was conducted to compare the novelty score-based multivariate control charts under both normal and non-normal scenarios. The simulation showed that in both normal and lognormal scenarios, D-max-based control charts produced the most promising results. In skewed distribution with high kurtosis non-normal scenarios, HNS- and K-2-based control charts performed best. In symmetric with kurtosis non-normal scenarios, local T-2-based control charts outperformed the others. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | TAYLOR & FRANCIS INC | - |
dc.subject | STATISTICAL PROCESS-CONTROL | - |
dc.subject | DISTANCE | - |
dc.subject | SHIFTS | - |
dc.title | Comparison of Novelty Score- Based Multivariate Control Charts | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Seoung Bum | - |
dc.identifier.doi | 10.1080/03610918.2013.809098 | - |
dc.identifier.scopusid | 2-s2.0-84908621006 | - |
dc.identifier.wosid | 000343647300002 | - |
dc.identifier.bibliographicCitation | COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, v.44, no.5, pp.1126 - 1143 | - |
dc.relation.isPartOf | COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION | - |
dc.citation.title | COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION | - |
dc.citation.volume | 44 | - |
dc.citation.number | 5 | - |
dc.citation.startPage | 1126 | - |
dc.citation.endPage | 1143 | - |
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.keywordPlus | DISTANCE | - |
dc.subject.keywordPlus | SHIFTS | - |
dc.subject.keywordAuthor | Data mining | - |
dc.subject.keywordAuthor | Novelty score | - |
dc.subject.keywordAuthor | Multivariate control charts | - |
dc.subject.keywordAuthor | Quality control | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(02841) 서울특별시 성북구 안암로 14502-3290-1114
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.