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The k-step spatial sign covariance matrix

Journal Contribution - Journal Article

The Sign Covariance Matrix is an orthogonal equivariant estimator of
multivariate scale. It is often used as an easy-to-compute and highly robust estimator.
In this paper we propose a k-step version of the Sign Covariance Matrix, which
improves its efficiency while keeping the maximal breakdown point. If k tends to
infinity, Tyler's M-estimator is obtained. It turns out that even for very low values of
k, one gets almost the same efficiency as Tyler's M-estimator.
Journal: Advances in Data Analysis and Classification
ISSN: 1862-5347
Volume: 4
Pages: 137-150
Number of pages: 13
Publication year:2010
Keywords:Breakdown point, Multivariate analysis, Principal components, Applied mathematics
  • Scopus Id: 77955849404