The monitoring of serially independent or autocorrelated count processes is considered, having a Poisson or (negative) binomial marginal distribution under in-control conditions. Utilizing the corresponding Stein identities, exponentially weighted moving-average (EWMA) control charts are constructed, which can be flexibly adapted to uncover zero inflation, over- or underdispersion. The proposed Stein EWMA charts' performance is investigated by simulations, and their usefulness is demonstrated by a real-world data example from health surveillance.

翻译：考虑了对序列独立或自相关计数过程的监控，这些过程在受控状态下具有泊松或（负）二项式边际分布。利用相应的Stein恒等式，构建了指数加权移动平均（EWMA）控制图，该图可灵活调整以揭示零膨胀、过离散或欠离散现象。通过模拟研究了所提出的Stein EWMA图的性能，并通过健康监测领域的真实数据实例证明了其实用性。