Publication
Title
Generalized spherical principal component analysis
Author
Abstract
Outliers contaminating data sets are a challenge to statistical estimators. Even a small fraction of outlying observations can heavily influence most classical statistical methods. In this paper we propose generalized spherical principal component analysis, a new robust version of principal component analysis that is based on the generalized spatial sign covariance matrix. Theoretical properties of the proposed method including influence functions, breakdown values and asymptotic efficiencies are derived. These theoretical results are complemented with an extensive simulation study and two real-data examples. We illustrate that generalized spherical principal component analysis can combine great robustness with solid efficiency properties, in addition to a low computational cost.
Language
English
Source (journal)
Statistics and computing. - London, 1991, currens
Publication
London : Chapman & Hall , 2024
ISSN
0960-3174 [print]
1573-1375 [online]
DOI
10.1007/S11222-024-10413-9
Volume/pages
34 (2024) , p. 1-20
Article Reference
104
Full text (Publisher's DOI)
Full text (open access)
The author-created version that incorporates referee comments and is the accepted for publication version Available from 23.09.2024
Full text (publisher's version - intranet only)
UAntwerpen
Faculty/Department
Research group
Project info
Robust Directed Acyclic Graph Learning for Causal Modeling.
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Record
Identifier
Creation 02.04.2024
Last edited 03.04.2024
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