Detection of Aberrant Observations in a Background of an Unknown Multidimensional Gaussian Distribution

Abstract

An exploratory iterative technique for the detection of aberrant observations on a background of a multidimensional Gaussian distribution is described. Its development was motivated by the analysis of a set of three measurements reflecting the acid-base metabolism in the blood of 2,402 intensive care patients. This new, three-dimensional treatment of such data yields a meaningful description. A technical evaluation of the method, using artificially generated data is also presented. It is shown that the model parameters of the underlying Gaussian distributions are determined with good accuracy and that the accuracy with which the contamination is estimated increases with increasing distance of the contaminating observations from the mean.

Key-Words

Normality Assessment - Outlier Detection - Acid-Base Metabolism

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