A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Robustifying principal component analysis with spatial sign vectors
Tekijät: Taskinen S, Koch I, Oja H
Kustantaja: ELSEVIER SCIENCE BV
Julkaisuvuosi: 2012
Journal: Statistics and Probability Letters
Tietokannassa oleva lehden nimi: STATISTICS & PROBABILITY LETTERS
Lehden akronyymi: STAT PROBABIL LETT
Vuosikerta: 82
Numero: 4
Aloitussivu: 765
Lopetussivu: 774
Sivujen määrä: 10
ISSN: 0167-7152
DOI: https://doi.org/10.1016/j.spl.2012.01.001
Tiivistelmä
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. (C) 2012 Elsevier B.V. All rights reserved.
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. (C) 2012 Elsevier B.V. All rights reserved.
Turun yliopiston Tutkimustietojärjestelmän portaali