Robustifying principal component analysis with spatial sign vectors

Sara Taskinen, Inge Koch and Hannu Oja

Statistics & Probability Letters, 2012, vol. 82, issue 4, 765-774

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.

Keywords: Affine equivariance; Efficiency; Influence function; Robustness; Spatial sign vector (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (11)

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DOI: 10.1016/j.spl.2012.01.001

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