Semiparametrically efficient rank-based inference for shape II. Optimal R-estimation of shape

Abstract

A class of R-estimators based on the concepts of multivariate signed ranks and the optimal rank-based tests developed in Hallin and Paindaveine [Ann. Statist. 34 (2006)] is proposed for the estimation of the shape matrix of an elliptical distribution. These R-estimators are root-n consistent under any radial density g, without any moment assumptions, and semiparametrically efficient at some prespecified density f. When based on normal scores, they are uniformly more efficient than the traditional normal-theory estimator based on empirical covariance matrices (the asymptotic normality of which, moreover, requires finite moments of order four), irrespective of the actual underlying elliptical density. They rely on an original rank-based version of Le Cam's one-step methodology which avoids the unpleasant nonparametric estimation of cross-information quantities that is generally required in the context of R-estimation. Although they are not strictly affine-equivariant, they are shown to be equivariant in a weak asymptotic sense. Simulations confirm their feasibility and excellent finite-sample performances.

Published at http://dx.doi.org/10.1214/009053606000000948 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)

References in corpus (4)

• Semiparametrically efficient rank-based inference for shape I. optimal rank-based tests for sphericity
• Semiparametrically efficient rank-based inference for shape II. Optimal R-estimation of shape
• Rank-based optimal tests of the adequacy of an elliptic VARMA model
• Serial and nonserial sign-and-rank statistics: asymptotic representation and asymptotic normality

Cited by in corpus (14)

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• Semiparametrically efficient rank-based inference for shape II. Optimal R-estimation of shape
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• Robust self-tuning semiparametric PCA for contaminated elliptical distribution
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