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Variable sampling interval EWMA chart for multivariate coefficient of variation
Communications in Statistics - Theory and Methods ( IF 0.6 ) Pub Date : 2020-09-10 , DOI: 10.1080/03610926.2020.1818100
Heba N. Ayyoub 1 , Michael B. C. Khoo 1 , Sajal Saha 2 , Ming Ha Lee 3
Affiliation  

Abstract

A control chart for monitoring the multivariate coefficient of variation (MCV) is used when the focus is on monitoring the ratio of relative multivariate variability to the mean of a multivariate process. The MCV chart is useful in process monitoring when practitioners are not interested in the consistency of the mean vector or covariance matrix. This study proposes a one-sided upward variable sampling interval (VSI) exponentially weighted moving average (EWMA) chart to detect increasing shifts in MCV squared (γ2) and shows the derivation of formulae to evaluate the performance of the VSI EWMA-γ2 chart by using the Markov chain approach. Comparative investigations show that the proposed chart outperforms existing MCV charts in detecting shifts in process MCV. A numerical example that uses real data reveals that the proposed chart performs well in actual applications.



中文翻译:

多元变异系数的可变采样间隔 EWMA 图

摘要

当重点是监控相对多变量变异性与多变量过程均值的比率时,使用用于监控多变量变异系数 (MCV) 的控制图。当从业者对均值向量或协方差矩阵的一致性不感兴趣时​​,MCV 图在过程监控中很有用。本研究提出了一种单边向上变量采样间隔 (VSI) 指数加权移动平均 (EWMA) 图来检测 MCV 平方的增加变化(γ2)并显示了评估 VSI EWMA 性能的公式的推导-γ2使用马尔可夫链方法绘制图表。比较研究表明,在检测过程 MCV 的变化方面,建议的图表优于现有的 MCV 图表。使用真实数据的数值示例表明,所提出的图表在实际应用中表现良好。

更新日期:2020-09-10
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