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Rank covariance matrix estimation of a partially known covariance matrixPrimeFaces.cw("AccordionPanel","widget_formSmash_some",{id:"formSmash:some",widgetVar:"widget_formSmash_some",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_all",{id:"formSmash:all",widgetVar:"widget_formSmash_all",multiple:true});
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PrimeFaces.cw("AccordionPanel","widget_formSmash_responsibleOrgs",{id:"formSmash:responsibleOrgs",widgetVar:"widget_formSmash_responsibleOrgs",multiple:true}); 2008 (English)In: Journal of Statistical Planning and Inference, ISSN 0378-3758, Vol. 138, no 12, 3667-3673 p.Article in journal (Refereed) Published
##### Abstract [en]

##### Place, publisher, year, edition, pages

2008. Vol. 138, no 12, 3667-3673 p.
##### Keyword [en]

multivariate ranks, rank covariance matrix, marginal rank covariance matrix, elliptical distributions, affine equivariance
##### National Category

Mathematics
##### Identifiers

URN: urn:nbn:se:uu:diva-97603DOI: 10.1016/j.jspi.2007.11.015ISI: 000259755600004OAI: oai:DiVA.org:uu-97603DiVA: diva2:172612
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Available from: 2008-10-10 Created: 2008-10-10 Last updated: 2012-07-26Bibliographically approved
##### In thesis

Classical multivariate methods are often based on the sample covariance matrix, which is very sensitive to outlying observations. One alternative to the covariance matrix is the affine equivariant rank covariance matrix (RCM) that has been studied in Visuri et al. [2003. Affine equivariant multivariate rank methods.]. Statist. Plann. Inference 114, 161-185]. In this article we assume that the covariance matrix is partially known and study how to estimate the corresponding RCM. We use the properties that the RCM is affine equivariant and that the RCM is proportional to the inverse of the regular covariance matrix, and hence reduce the problem of estimating the original RCM to estimating marginal rank covariance matrices. This is a great computational advantage when the dimension of the original data vector is large.

1. Rank Estimation in Elliptical Models: Estimation of Structured Rank Covariance Matrices and Asymptotics for Heteroscedastic Linear Regression$(function(){PrimeFaces.cw("OverlayPanel","overlay172615",{id:"formSmash:j_idt647:0:j_idt651",widgetVar:"overlay172615",target:"formSmash:j_idt647:0:parentLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

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