Applications of shift detection are of interest in several disciplines. Sufficient reduction (SR) methods have been developed for detecting a shift in a multivariate process under different conditions in several studies. However, all methods were proposed to detect only a constant, but persistent, mean shift. In practice, there might be other types of (mean) shift to be considered. Our purpose here is to investigate the robustness of SR methods for detecting different types of shift in a multivariate process. Four shift types are considered. The performances of the SR methods are compared against other statistical techniques used in multivariate process control. The evaluation was conducted via simulation by estimating four measures. The results show that in a process of independent observations the Wessman method performs well for detecting all sizes of single spike shift and small constant, linear, and exponential shifts. In an autocorrelated process the Parallel, Frisén, and Wessman methods produce a high number of false alarms. The Siripanthana and Stillman method gives shorter delays for detecting small shifts of all types, while the vector autoregressive chart gives a shorter delay for a large constant shift. The applications of SR methods to real health surveillance data are illustrated, with examples from food poisoning and pneumonia monitoring in Thailand.

中文翻译：

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用于检测多元过程中各种类型偏移的充分约简方法的鲁棒性

移位检测的应用在多个学科中受到关注。在几项研究中，已经开发了足够的减少 (SR) 方法来检测多变量过程在不同条件下的变化。然而，所有方法都被提议用来检测一个恒定但持久的均值漂移。在实践中，可能需要考虑其他类型的（均值）偏移。我们在这里的目的是研究 SR 方法在多变量过程中检测不同类型偏移的稳健性。考虑了四种班次类型。将 SR 方法的性能与多变量过程控制中使用的其他统计技术进行比较。评估是通过模拟通过估计四个措施来进行的。结果表明，在独立观察的过程中，Wessman 方法在检测各种大小的单峰位移和小的常数、线性和指数位移方面表现良好。在自相关过程中，Parallel、Frisén 和 Wessman 方法会产生大量误报。Siripanthana 和 Stillman 方法为检测所有类型的小位移提供更短的延迟，而矢量自回归图为大的恒定位移提供更短的延迟。以泰国的食物中毒和肺炎监测为例，说明了 SR 方法在实际健康监测数据中的应用。Siripanthana 和 Stillman 方法为检测所有类型的小位移提供更短的延迟，而矢量自回归图为大的恒定位移提供更短的延迟。以泰国的食物中毒和肺炎监测为例，说明了 SR 方法在实际健康监测数据中的应用。Siripanthana 和 Stillman 方法为检测所有类型的小位移提供更短的延迟，而矢量自回归图为大的恒定位移提供更短的延迟。以泰国的食物中毒和肺炎监测为例，说明了 SR 方法在实际健康监测数据中的应用。