Robustifying principal component analysis with spatial sign vectors
Abstract
In this paper, we apply orthogonally equivariant spatial sign covariance matrices as well as their affine equivariant counterparts in principal component analysis. The influence functions and asymptotic covariance matrices of eigenvectors based on robust covariance estimators are derived in order to compare the robustness and efficiency properties. We show in particular that the estimators that use pairwise differences of the observed data have very good efficiency properties, providing practical robust alternatives to classical sample covariance matrix based methods.
Main Authors
Format
Articles
Research article
Published
2012
Series
Subjects
Publication in research information system
Publisher
Elsevier
The permanent address of the publication
https://urn.fi/URN:NBN:fi:jyu-201211293124Use this for linking
Review status
Peer reviewed
ISSN
0167-7152
DOI
https://doi.org/10.1016/j.spl.2012.01.001
Language
English
Published in
Statistics and Probability Letters
Citation
- Taskinen, S., Koch, I., & Oja, H. (2012). Robustifying principal component analysis with spatial sign vectors. Statistics and Probability Letters, 82(4), 765-774. https://doi.org/10.1016/j.spl.2012.01.001
License
Copyright© Elsevier. This is a pre-print version of an article whose final and definitive form has been published by Elsevier.