@techreport{b3c069e53f34475a9c1b154904836bf2,

title = "The K-Step Spatial Sign Covariance Matrix",

abstract = "The Sign Covariance Matrix is an orthogonal equivariant estimator of mul- tivariate 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 e±ciency 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 e±ciency as Tyler's M-estimator.",

author = "C. Croux and C. Dehon and A. Yadine",

note = "Subsequently published in Advances in Data Analysis and Classification, 2010 Pagination: 13",

year = "2010",

language = "English",

volume = "2010-41",

series = "CentER Discussion Paper",

publisher = "Econometrics",

type = "WorkingPaper",

institution = "Econometrics",

}