Operator norm consistent estimation of large-dimensional sparse covariance matrices

TL;DR

This paper introduces a new operator norm consistent estimator for large-dimensional sparse covariance matrices, addressing the limitations of traditional estimators in high-dimensional settings with sparsity assumptions.

Contribution

It develops a novel estimator that is consistent in operator norm for large p and n, and proposes a spectral-compatible sparsity notion independent of variable ordering.

Findings

Estimator is consistent in operator norm as p and n grow large.

Eigenvalues and eigenspaces are consistently estimated.

Proposes a spectral-compatible sparsity measure.

Abstract

Estimating covariance matrices is a problem of fundamental importance in multivariate statistics. In practice it is increasingly frequent to work with data matrices $X$ of dimension $n\times p$, where $p$ and $n$ are both large. Results from random matrix theory show very clearly that in this setting, standard estimators like the sample covariance matrix perform in general very poorly. In this "large $n$, large $p$" setting, it is sometimes the case that practitioners are willing to assume that many elements of the population covariance matrix are equal to 0, and hence this matrix is sparse. We develop an estimator to handle this situation. The estimator is shown to be consistent in operator norm, when, for instance, we have $p\asymp n$ as $n\to\infty$. In other words the largest singular value of the difference between the estimator and the population covariance matrix goes to zero. This implies consistency of all the eigenvalues and consistency of eigenspaces associated to isolated eigenvalues. We also propose a notion of sparsity for matrices, that is, "compatible" with spectral analysis and is independent of the ordering of the variables.