Robustifying principal component analysis with spatial sign vectors

Sara Taskinen, Inge Koch, Hannu Oja

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)765-774
Number of pages10
JournalStatistics and Probability Letters
Issue number4
Publication statusPublished - 1 Apr 2012
Externally publishedYes


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