In the context of a disease outbreak detection, a prime interest is to only detect increases in the process mean. It is thus desirable to have a directionally sensitive multivariate chart that can effectively detect either increases or decreases in the process mean vector. In this paper, with a suitable transformation that truncates multivariate observations either above or below the process mean vector, we propose one-sided and two one-sided MCUSUM charts for monitoring the mean of a multivariate normal process. Among the proposed charts, the one-sided MCUSUM charts are directionally sensitive, while the two one-sided MCUSUM charts are directionally insensitive. In addition, the fast initial response feature is also incorporated into the proposed charts to enhance their sensitivities against initial process shifts. The run length characteristics of these control charts are computed with the Monte Carlo simulation. Based on the run length comparisons, it is found that the proposed charts are more sensitive than the existing charts when detecting moderate-to-large shifts in the process mean. The proposed charts are also applied on real datasets to support the theory.

中文翻译：

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方向敏感的 MCUSUM 均值图表

在疾病爆发检测的背景下，主要兴趣是仅检测过程均值的增加。因此，希望有一个方向敏感的多元图表，它可以有效地检测过程平均向量的增加或减少。在本文中，通过适当的变换截断过程均值向量上方或下方的多变量观测值，我们提出了单边和两个单边 MCUSUM 图来监测多元正态过程的均值。在建议的图表中，单面 MCUSUM 图表是方向敏感的，而两个单面 MCUSUM 图表是方向不敏感的。此外，快速初始响应功能也被纳入提议的图表中，以提高其对初始过程变化的敏感性。这些控制图的运行长度特性是用蒙特卡罗模拟计算的。根据运行长度比较，发现在检测过程均值中到大的变化时，建议的图表比现有图表更敏感。建议的图表也应用于真实数据集以支持该理论。