This review covers recent results concerning the estimation of large covariance matrices
using tools from Random Matrix Theory (RMT). We introduce several RMT methods and …

This article studies the Gram random matrix model G= 1 T Σ⊺ Σ, Σ= σ (WX), classically found
in the analysis of random feature maps and random neural networks, where X=[x ₁,..., xT]∈ …

Covariance matrix estimation is one of the most important problems in statistics. To
accommodate the complexity of modern datasets, it is desired to have estimation procedures …

Estimation of the covariance matrix has attracted a lot of attention of the statistical research
community over the years, partially due to important applications such as principal …

We provide a survey of recent results on covariance estimation for heavy-tailed distributions.
By unifying ideas scattered in the literature, we propose user-friendly methods that facilitate …

This article studies two regularized robust estimators of scatter matrices proposed (and
proved to be well defined) in parallel in Chen et al.(2011) and Pascal et al.(2013), based on …

We study the design of portfolios under a minimum risk criterion. The performance of the
optimized portfolio relies on the accuracy of the estimated covariance matrix of the portfolio …

We consider robust covariance estimation with an emphasis on Tyler's M-estimator. This
method provides accurate inference of an unknown covariance in non-standard settings …

In a seminal paper, Tyler suggests an M-estimator for shape, which is now known as Tyler's
shape matrix. Tyler's shape matrix is increasingly popular due to its nice statistical …

We propose estimating the scale parameter (mean of the eigenvalues) of the scatter matrix
of an unspecified elliptically symmetric distribution using weights obtained by solving Tyler's …