The occurrence of undetected outliers severely disrupts model building procedures and produces unreliable results. This topic has been widely addressed in the statistical literature. However, little attention has been paid to determine how large an outlier has to be for correct detection of both time and magnitude to safely take place. This issue has been the object of research mainly in geodesy. In this paper, the minimal detectable bias concept is extended to vector time series data, and the risk of accepting an outlier as a clean observation is evaluated according to both the size and power of the statistical tests. This approach seems able to deal with the difficult issues known as masking and swamping. The proposed measure of outlier identifiability helps to determine if any configurations of multiple outliers, also occurring in patches, are easily detectable.

中文翻译：

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时间序列中的异常值可识别性

未检测到的异常值的出现严重破坏了模型构建过程，并产生了不可靠的结果。在统计文献中已经广泛地解决了这个话题。但是，很少有人注意确定异常值必须多大才能正确检测时间和大小，才能安全发生。这个问题一直是大地测量学的研究对象。在本文中，最小可检测偏差概念被扩展到矢量时间序列数据，并且根据统计检验的大小和功效评估了接受异常值作为干净观测值的风险。这种方法似乎能够处理被称为掩蔽和沼泽的难题。提出的离群值可识别性度量有助于确定是否也存在补丁中的多个离群值的任何配置，