Publication
Title
Outlier detection for skewed data
Author
Abstract
Most outlier detection rules for multivariate 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 [1] which is based on the concept of half space depth. We illustrate our outlier detection method on several simulated and real data. Copyright (c) 2008 John Wiley & Sons, Ltd.
Language
English
Source (journal)
Journal of chemometrics. - Chichester
Source (book)
International Chemometric Conference (Conferentia Chemometrica 2007), SEP 02-05, 2007, Budapest, HUNGARY
Publication
Chichester : 2008
ISSN
0886-9383
Volume/pages
22:3-4(2008), p. 235-246
ISI
000255294600009
Full text (Publisher's DOI)
Full text (publisher's version - intranet only)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identification
Creation 03.01.2013
Last edited 20.09.2017
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