Title
A StahelDonoho estimator based on huberized outlyingnessA StahelDonoho estimator based on huberized outlyingness
Author
Faculty/Department
Faculty of Sciences. Mathematics and Computer Science
Research group
Department of Mathematics - Computer Sciences
Department of Mathematics and Computer Science - other
Publication type
article
Publication
Amsterdam,
Subject
Mathematics
Computer. Automation
Source (journal)
Computational statistics and data analysis. - Amsterdam
Volume/pages
56(2012):3, p. 531-542
ISSN
0167-9473
ISI
000298122600008
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
Abstract
The StahelDonoho estimator is defined as a weighted mean and covariance, where the weight of each observation depends on a measure of its outlyingness. In high dimensions, it can easily happen that a number of outlying measurements are present in such a way that the majority of observations are contaminated in at least one of their components. In these situations, the StahelDonoho estimator has difficulties in identifying the actual outlyingness of the contaminated observations. An adaptation of the StahelDonoho estimator is presented in which the data are huberized before the outlyingness is computed. It is shown that the huberized outlyingness better reflects the actual outlyingness of each observation towards the non-contaminated observations. Therefore, the resulting adapted StahelDonoho estimator can better withstand large numbers of outliers. It is demonstrated that the StahelDonoho estimator based on huberized outlyingness works especially well when the data are heavily contaminated.
E-info
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