{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,5,7]],"date-time":"2024-05-07T16:57:52Z","timestamp":1715101072974},"reference-count":31,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2021,11,9]],"date-time":"2021-11-09T00:00:00Z","timestamp":1636416000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Quality & Reliability Eng"],"published-print":{"date-parts":[[2022,3]]},"abstract":"Abstract<\/jats:title>Multivariate charts for process dispersion detect changes in the variance\u2010covariance matrix of a process. Most of the existing multivariate charts for monitoring the dispersion of individual observations were designed based on exponentially weighted moving average (EWMA) charting schemes. However, an alternative to the EWMA scheme is the cumulative sum (CUSUM) control chart, which has proven to be better in some cases. In the last decades, few studies have been conducted on methods based on multivariate CUSUM (MCUSUM) schemes to monitor the covariance matrix of individual observations. Consequently, we propose a new MCUSUM dispersion chart. Besides, most of the existing methods have been developed by assuming that the process parameters are known and that the process distribution is normal; these assumptions are not always true in practice. Hence, we compare the performance of the proposed chart and its counterparts based on the estimation effects under normal and non\u2010normal distributions. The results show that the proposed chart outperforms the other charts in terms of minor shifts in the process. Similarly, the proposed chart is the most robust to the normality assumption among the compared charts. The average value of the conditional average run length was used as the performance measure. Finally, the proposed method was also implemented with a simulated dataset to support the stated proposal findings.<\/jats:p>","DOI":"10.1002\/qre.3017","type":"journal-article","created":{"date-parts":[[2021,11,9]],"date-time":"2021-11-09T17:52:52Z","timestamp":1636480372000},"page":"834-847","update-policy":"http:\/\/dx.doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["A new multivariate CUSUM chart for monitoring of covariance matrix with individual observations under estimated parameter"],"prefix":"10.1002","volume":"38","author":[{"given":"Jimoh Olawale","family":"Ajadi","sequence":"first","affiliation":[{"name":"Department of Mathematics College of Computing and Mathematics King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia"}]},{"given":"Angus","family":"Wong","sequence":"additional","affiliation":[{"name":"School of Applied Science Macao Polytechnic Institute Macao China"},{"name":"Department of Technology School of Science and Technology Hong Kong Metropolitan University Kowloon Hong Kong"}]},{"given":"Tahir","family":"Mahmood","sequence":"additional","affiliation":[{"name":"Industrial and Systems Engineering Department College of Computing and Mathematics King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia"},{"name":"Interdisciplinary Research Center for Smart Mobility & Logistics King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia"}]},{"given":"Kevin","family":"Hung","sequence":"additional","affiliation":[{"name":"School of Applied Science Macao Polytechnic Institute Macao China"},{"name":"Department of Technology School of Science and Technology Hong Kong Metropolitan University Kowloon Hong Kong"}]}],"member":"311","published-online":{"date-parts":[[2021,11,9]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1002\/qre.2887"},{"key":"e_1_2_9_3_1","volume-title":"The Encyclopedia of Statistical Sciences","author":"Alt 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