Identification of Outliers

One of the most vexing of problems in data analysis is the determination of whether or not to discard some observations because they are inconsistent with the rest of the observations and/or the probability distribution assumed to be the underlying distribution of the data. One direction of research activity related to this problem is that of the study of robust statistical procedures (cf. Huber [1981]), primarily procedures for estimating population parameters which are insensitive to the effect of “outliers”, i.e., observations inconsistent with the assumed model of the random process generating the observations. Typically, the robust procedure involves some “trimming” or down-weighting procedure, wherein some fraction of the extreme observations are automatically eliminated or given less weight to guard against the potential effect of outliers.

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