Outlier identifiability in time series
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.