Robust Semiparametric Efficient Estimators in Elliptical Distributions

Abstract

Covariance matrices play a major role in statistics, signal processing and machine learning applications. This paper focuses on the \textit{semiparametric} covariance/scatter matrix estimation problem in elliptical distributions. The class of elliptical distributions can be seen as a semiparametric model where the finite-dimensional vector of interest is given by the location vector and by the (vectorized) covariance/scatter matrix, while the density generator represents an infinite-dimensional nuisance function. The main aim of this work is then to provide possible estimators of the finite-dimensional parameter vector able to reconcile the two dichotomic concepts of \textit{robustness} and (semiparametric) \textit{efficiency}. An $R$-estimator satisfying these requirements has been recently proposed by Hallin, Oja and Paindaveine for real-valued elliptical data by exploiting the Le Cam's theory of \textit{one-step efficient estimators} and the \textit{rank-based statistics}. In this paper, we firstly recall the building blocks underlying the derivation of such real-valued $R$-estimator, then its extension to complex-valued data is proposed. Moreover, through numerical simulations, its estimation performance and robustness to outliers are investigated in a finite-sample regime.

arXiv commentThis paper has been submitted to IEEE Transactions on Signal Processing (12 pages, 6 figures and supporting material)

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• Robust M-Estimation Based Bayesian Cluster Enumeration for Real Elliptically Symmetric Distributions
• Robust self-tuning semiparametric PCA for contaminated elliptical distribution
• Joint Estimation of Location and Scatter in Complex Elliptical Distributions: A robust semiparametric and computationally efficient $R$-estimator of the shape matrix