If monitoring Poisson count data for a possible mean shift (while the Poisson distribution is preserved), then the ordinary Poisson exponentially weighted moving-average (EWMA) control chart proved to be a good solution. In practice, however, mean shifts might occur in combination with further changes in the distribution family. Or due to a misspecification during Phase-I analysis, the Poisson assumption might not be appropriate at all. In such cases, the ordinary EWMA chart might not perform satisfactorily. Therefore, two novel classes of generalized EWMA charts are proposed, which utilize the so-called Stein-Chen identity and are thus sensitive to further distributional changes than just sole mean shifts. Their average run length (ARL) performance is investigated with simulations, where it becomes clear that especially the class of so-called "ABC-EWMA charts" shows an appealing ARL performance. The practical application of the novel Stein-Chen EWMA charts is illustrated with an application to count data from semiconductor manufacturing.

翻译：若监测服从泊松分布的计数数据是否发生均值偏移（且假定分布族保持不变），传统泊松指数加权移动平均（EWMA）控制图已被证明是有效的解决方案。然而实践中，均值偏移常伴随分布族的其他变化，或由于第一阶段分析中的误设定导致泊松假设完全不适用。在此类情形下，传统EWMA控制图可能表现不佳。为此，本文提出两类新型广义EWMA控制图，通过引入Stein-Chen恒等式使其对除均值偏移以外的分布变化更敏感。通过仿真研究平均运行链长（ARL）性能，结果表明其中名为"ABC-EWMA控制图"的类别展现出优越的ARL表现。最后，结合半导体制造中的计数数据实例，阐释了新型Stein-Chen EWMA控制图的实际应用方法。