Publication
Title
A generalized spatial sign covariance matrix
Author
Abstract
The well-known spatial sign covariance matrix (SSCM) carries out a radial transform which moves all data points to a sphere, followed by computing the classical covariance matrix of the transformed data. Its popularity stems from its robustness to outliers, fast computation, and applications to correlation and principal component analysis. In this paper we study more general radial functions. It is shown that the eigenvectors of the generalized SSCM are still consistent and the ranks of the eigenvalues are preserved. The influence function of the resulting scatter matrix is derived, and it is shown that its asymptotic breakdown value is as high as that of the original SSCM. A simulation study indicates that the best results are obtained when the inner half of the data points are not transformed and points lying far away are moved to the center.
Language
English
Source (journal)
Journal of multivariate analysis. - New York, N.Y.
Publication
New York, N.Y. : 2019
ISSN
0047-259X
DOI
10.1016/J.JMVA.2018.11.010
Volume/pages
171 (2019) , p. 94-111
ISI
000463305300007
Full text (Publisher's DOI)
Full text (open access)
UAntwerpen
Research group
Publication type
Subject
External links
Web of Science
Record
Identifier
Creation 27.02.2024
Last edited 07.03.2024
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