0000000001038616

AUTHOR

Inge Koch

showing 2 related works from this author

Robustifying principal component analysis with spatial sign vectors

2012

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.

Statistics and ProbabilityMathematical optimizationEstimation of covariance matricesMatérn covariance functionCovariance functionCovariance matrixLaw of total covarianceApplied mathematicsRational quadratic covariance functionCovariance intersectionStatistics Probability and UncertaintyCovarianceMathematicsStatistics & Probability Letters
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Robustifying principal component analysis with spatial sign vectors

2012

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. peerReviewed

robustisuusvaikutusfunktiospatiaalinen merkkivektoriefficiencyinfluence functionAffiinisti ekvivarianttisuustehokkuusAffine equivariancerobustnessspatial sign vector
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