Properties of a new r-estimator of shape matrices

Stefano Fortunati, Alexandre Renaux, Frédéric Pascal

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper aims at presenting a simulative analysis of the main properties of a new R-estimator of shape matrices in Complex Elliptically Symmetric (CES) distributed observations. First proposed by Hallin, Oja and Paindaveine for the real-valued case and then extended to the complex field in our recent work, this R-estimator has the remarkable property to be, at the same time, distributionally robust and semiparametric efficient. Here, the efficiency of different possible configurations of this R-estimator are investigated by comparing the resulting Mean Square Error (MSE) with the Constrained Semiparametric Cramér-Rao Bound (CSCRB). Moreover, its robustness to outliers is assessed and compared with the one of the celebrated Tyler's estimator.

Original languageEnglish
Title of host publication28th European Signal Processing Conference, EUSIPCO 2020 - Proceedings
PublisherEuropean Signal Processing Conference, EUSIPCO
Pages2443-2447
Number of pages5
ISBN (Electronic)9789082797053
DOIs
Publication statusPublished - 24 Jan 2021
Externally publishedYes
Event28th European Signal Processing Conference, EUSIPCO 2020 - Amsterdam, Netherlands
Duration: 24 Aug 202028 Aug 2020

Publication series

NameEuropean Signal Processing Conference
Volume2021-January
ISSN (Print)2219-5491

Conference

Conference28th European Signal Processing Conference, EUSIPCO 2020
Country/TerritoryNetherlands
CityAmsterdam
Period24/08/2028/08/20

Keywords

  • CES distributions
  • R-estimator
  • Scatter matrix estimation
  • Semiparametric models

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