One-sided control charts for monitoring changes in the mean level are proposed in this paper. The proposed charts are given in the form of a homogeneously weighted moving average technique that provides efficient monitoring of small shifts in the mean level. The charts accumulate observations that are above the target (or mean value) and truncated the observations that are less than the target to the target value in their computations. Average run length comparisons of the proposed charts with the existing one-sided charts, based on the exponentially weighted moving average (EWMA) and cumulative sum (CUSUM) charts, show that the proposed charts are more efficient in detecting small shifts than the competing charts. We investigate the sensitivity of the charts to non-normality and show how they can be designed to be robust to non-normal distributions. We provide a step-by-step implementation of the proposed charts when their parameters are unknown and estimated from historical reference data sets. The advantage of the proposed charts over some existing one-sided charts is demonstrated via an illustrative example, involving monitoring mean lethal concentration (LC${}_{50}$) from a k-nearest neighbours (KNN) regression-based Quantitative Structure-Activity Relationships (QSAR) model that relates LC${}_{50}$ to eight molecular descriptors.

中文翻译：

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方向敏感的均匀加权移动平均控制图

本文提出了用于监测平均水平变化的单面控制图。建议的图表以均匀加权移动平均技术的形式给出，该技术可有效监控平均水平的微小变化。图表累积高于目标（或平均值）的观测值，并在计算中将低于目标值的观测值截断为目标值。基于指数加权移动平均 (EWMA) 和累积总和 (CUSUM) 图表，建议图表与现有单边图表的平均运行长度比较表明，建议图表在检测小偏移方面比竞争图表更有效. 我们研究了图表对非正态性的敏感性，并展示了如何将它们设计为对非正态分布具有鲁棒性。当它们的参数未知并从历史参考数据集估计时，我们提供了建议图表的逐步实施。所提议的图表相对于一些现有的单边图表的优势通过一个说明性示例得到证明，该示例涉及监测平均致死浓度 (LC${}_{50}$) 来自与 LC 相关的基于 k 最近邻 (KNN) 回归的定量结构活动关系 (QSAR) 模型${}_{50}$ 到八个分子描述符。