Title: Outlier detection for skewed data
Authors: Hubert, Mia ×
Van der Veeken, Stephan #
Issue Date: Mar-2008
Publisher: Wiley
Series Title: Journal of chemometrics vol:22 issue:3-4 pages:235-246
Abstract: Most outlier detection rules formultivariate data are based on the assumption of elliptical symmetry of the underlying
distribution. We propose an outlier detection method which does not need the assumption of symmetry and does
not rely on visual inspection. Our method is a generalization of the Stahel–Donoho outlyingness. The latter approach assigns to each observation a measure of outlyingness, which is obtained by projection pursuit techniques that only use univariate robust measures of location and scale. To allow skewness in the data, we adjust this measure of outlyingness by using a robust measure of skewness as well. The observations corresponding to an outlying value of the adjusted outlyingness (AO) are then considered as outliers. For bivariate data, our approach leads to two graphical representations. The first one is a contour plot of the AO values. We also construct an extension of the boxplot for bivariate data, in the spirit of the bagplot which is based on the concept of half space depth. We illustrate our outlier detection method on several simulated and real data.
ISSN: 0886-9383
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Statistics Section
× corresponding author
# (joint) last author

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