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A Control Chart for the Multivariate Coefficient of Variation

机译:多元变异系数的控制图

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Existing charts in the literature usually monitor either the mean or the variance of the process. However, in certain scenarios, the practitioner is not interested in the changes in the mean or the variance but is instead interested in monitoring the relative variability compared with the mean. This relative variability is called the coefficient of variation (CV). In the existing literature, none of the control charts that monitor the CV are applied for multivariate data. To fill this gap in research, this paper proposes a CV chart that monitors the CV for multivariate data. To the best of the authors' knowledge, this proposed chart is the first control chart for this purpose. The distributional properties of the sample CV for multivariate data and the procedures to implement the chart are presented in this paper. Formulae to compute the control limits, the average run length, the standard deviation of the run length, and the expected average run length for the case of unknown shift size are derived. From the numerical examples provided, the effects of the number of variables, the sample size, the shift size and the in-control value of the CV are studied. Finally, we demonstrate the usefulness and applicability of the proposed chart on real data. Copyright (c) 2015 John Wiley & Sons, Ltd.
机译:文献中的现有图表通常监视过程的均值或方差。但是,在某些情况下,从业人员对均值或方差的变化不感兴趣,而是对与均值相比的相对变化性感兴趣。这种相对可变性称为变异系数(CV)。在现有文献中,没有用于监视CV的控制图适用于多变量数据。为了填补这一研究空白,本文提出了一种CV图表,该图表可监视多变量数据的CV。就作者所知,该拟定图表是为此目的而设计的第一个控制图。本文介绍了多元数据样本CV的分布特性以及实现图表的过程。得出了用于计算控制极限,平均行程长度,行程长度的标准偏差以及预期的平均行程长度的公式,用于未知换挡尺寸的情况。从提供的数值示例中,研究了变量数量,样本大小,移位大小和CV的控制值的影响。最后,我们证明了所建议的图表在真实数据上的有用性和适用性。版权所有(c)2015 John Wiley&Sons,Ltd.

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