Estimation of the Covariance Matrix of Large Dimensional Data

TL;DR

This paper introduces a new method for estimating the covariance matrix of high-dimensional data, addressing limitations of traditional techniques by leveraging random matrix theory to improve accuracy and consistency.

Contribution

It extends Mestre's parametric framework by relaxing assumptions, jointly estimating eigenvalues and multiplicities, and analyzing variance through a Central Limit theorem.

Findings

Provides joint consistent estimates of eigenvalues and multiplicities.

Relaxes the separability assumption in covariance estimation.

Analyzes variance error using a Central Limit theorem.

Abstract

This paper deals with the problem of estimating the covariance matrix of a series of independent multivariate observations, in the case where the dimension of each observation is of the same order as the number of observations. Although such a regime is of interest for many current statistical signal processing and wireless communication issues, traditional methods fail to produce consistent estimators and only recently results relying on large random matrix theory have been unveiled. In this paper, we develop the parametric framework proposed by Mestre, and consider a model where the covariance matrix to be estimated has a (known) finite number of eigenvalues, each of it with an unknown multiplicity. The main contributions of this work are essentially threefold with respect to existing results, and in particular to Mestre's work: To relax the (restrictive) separability assumption, to provide joint consistent estimates for the eigenvalues and their multiplicities, and to study the variance error by means of a Central Limit theorem.