par Croux, Christophe 
;Dehon, Catherine ;Yadine, Abdelilah
Référence Advances in Data Analysis and Classification, 4, 2, page (137-150)
Publication Publié, 2010
;Dehon, Catherine ;Yadine, Abdelilah Référence Advances in Data Analysis and Classification, 4, 2, page (137-150)
Publication Publié, 2010
Article révisé par les pairs
| Résumé : | 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. © 2010 The Author(s). |
