Background on real and complex elliptically symmetric distributions

TL;DR

This chapter reviews real and complex elliptically symmetric distributions, discussing their properties, subclasses, and estimation methods, with a focus on moments, transformations, and asymptotic behavior of estimators.

Contribution

It provides a comprehensive overview of RES distributions, including complex variants, their properties, subclasses, and estimation techniques, highlighting recent theoretical insights.

Findings

Analysis of moments, transformations, and stability properties.

Discussion of estimation methods like M-estimators and Tyler's estimators.

Insights into asymptotic Gaussianity of scatter matrix estimators.

Abstract

This chapter presents a short overview of real elliptically symmetric (RES) distributions, complemented by circular complex elliptically symmetric (C-CES) and noncircular CES (NC-CES) distributions as complex representations of RES distributions. These distributions are both an extension of the multivariate Gaussian distribution and a multivariate extension of univariate symmetric distributions. They are equivalently defined through their characteristic functions and their stochastic representations, which naturally follow from the spherically symmetric distributions after affine transformations. Particular attention is paid to the absolutely continuous case and to the subclass of compound Gaussian distributions. Results related to moments, affine transformations, marginal and conditional distributions, and summation stability are also presented. Some well-known instances of RES distributions are provided with their main properties. Finally, the estimation of the symmetry center and scatter matrix is briefly discussed through the sample mean (SM), sample covariance matrix (SCM) estimate, maximum estimate (ML), $M$-estimators, and Tyler's $M$-estimators. Particular attention will be paid to the asymptotic Gaussianity of the $M$-estimators of the scatter matrix. To conclude, some hints about the Slepian-Bangs formula are provided.