Joint Estimation of Location and Scatter in Complex Elliptical Distributions: A robust semiparametric and computationally efficient $R$-estimator of the shape matrix

TL;DR

This paper introduces a robust, computationally efficient $R$-estimator for the shape matrix in complex elliptical distributions, addressing joint location and scatter estimation in a semiparametric framework.

Contribution

It develops a novel, memory-efficient implementation of the $R$-estimator and proposes a joint estimator combining Tyler’s M-estimator with the $R$-estimator, advancing robust estimation methods.

Findings

The $R$-estimator is computationally efficient and memory-saving.

The joint estimator's MSE approaches the semiparametric Cramér-Rao bound.

The method effectively handles unknown density generators in complex elliptical models.

Abstract

The joint estimation of the location vector and the shape matrix of a set of independent and identically Complex Elliptically Symmetric (CES) distributed observations is investigated from both the theoretical and computational viewpoints. This joint estimation problem is framed in the original context of semiparametric models allowing us to handle the (generally unknown) density generator as an \textit{infinite-dimensional} nuisance parameter. In the first part of the paper, a computationally efficient and memory saving implementation of the robust and semiparmaetric efficient $R$-estimator for shape matrices is derived. Building upon this result, in the second part, a joint estimator, relying on the Tyler's $M$-estimator of location and on the $R$-estimator of shape matrix, is proposed and its Mean Squared Error (MSE) performance compared with the Semiparametric Cram\'{e}r-Rao Bound (CSCRB).