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題名 A phase II multivariate EWMA chart for monitoring multi-dimensional ratios of process means with individual observations
作者 楊素芬
Yang, Su-Fen;Yeh, Arthur B.;Chou, Chiu-Chuan
貢獻者 統計系
關鍵詞 Multi-dimensional ratios of means; Multivariate distribution; Phase II monitoring; Unbiased estimators
日期 2023-09
上傳時間 13-Dec-2023 13:55:06 (UTC+8)
摘要 In recent years, there has been a resurgence in the development of control charts for monitoring the mean of the ratio of two correlated variables. However, most of the existing research has focused on the univariate mean of the ratio of two correlated variables under the assumption that the process follows a bivariate normal distribution. Furthermore, most of the existing research utilize biased estimators of the mean of the ratio of two correlated variables to develop control charts. More importantly, in certain applications, critical quality characteristics to be closely monitored are actually the ratios of the means of correlated variables, in that the process is considered to be stable as long as the ratios of the means of correlated variables remain constant at given levels, regardless of how each variable changes. We are thus motivated in this study to develop an exponentially weighted moving average (EWMA) based Phase II control chart for monitoring multi-dimensional ratios of the means of correlated variables. In this study, we provide a general framework for estimating parameters and control limits which is applicable without having to assume that the process follows a multivariate normal distribution. The performance of the proposed chart is evaluated under different multivariate distributions. Finally, a real data application of the proposed chart is presented to illustrate the practicality of the proposed chart.
關聯 Computers & Industrial Engineering, Vol.183, 109490
資料類型 article
DOI https://doi.org/10.1016/j.cie.2023.109490
dc.contributor 統計系
dc.creator (作者) 楊素芬
dc.creator (作者) Yang, Su-Fen;Yeh, Arthur B.;Chou, Chiu-Chuan
dc.date (日期) 2023-09
dc.date.accessioned 13-Dec-2023 13:55:06 (UTC+8)-
dc.date.available 13-Dec-2023 13:55:06 (UTC+8)-
dc.date.issued (上傳時間) 13-Dec-2023 13:55:06 (UTC+8)-
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/148698-
dc.description.abstract (摘要) In recent years, there has been a resurgence in the development of control charts for monitoring the mean of the ratio of two correlated variables. However, most of the existing research has focused on the univariate mean of the ratio of two correlated variables under the assumption that the process follows a bivariate normal distribution. Furthermore, most of the existing research utilize biased estimators of the mean of the ratio of two correlated variables to develop control charts. More importantly, in certain applications, critical quality characteristics to be closely monitored are actually the ratios of the means of correlated variables, in that the process is considered to be stable as long as the ratios of the means of correlated variables remain constant at given levels, regardless of how each variable changes. We are thus motivated in this study to develop an exponentially weighted moving average (EWMA) based Phase II control chart for monitoring multi-dimensional ratios of the means of correlated variables. In this study, we provide a general framework for estimating parameters and control limits which is applicable without having to assume that the process follows a multivariate normal distribution. The performance of the proposed chart is evaluated under different multivariate distributions. Finally, a real data application of the proposed chart is presented to illustrate the practicality of the proposed chart.
dc.format.extent 105 bytes-
dc.format.mimetype text/html-
dc.relation (關聯) Computers & Industrial Engineering, Vol.183, 109490
dc.subject (關鍵詞) Multi-dimensional ratios of means; Multivariate distribution; Phase II monitoring; Unbiased estimators
dc.title (題名) A phase II multivariate EWMA chart for monitoring multi-dimensional ratios of process means with individual observations
dc.type (資料類型) article
dc.identifier.doi (DOI) 10.1016/j.cie.2023.109490
dc.doi.uri (DOI) https://doi.org/10.1016/j.cie.2023.109490