Noise Estimation in the Spiked Covariance Model

TL;DR

This paper introduces a new noise estimation method for high-dimensional spiked covariance matrices, demonstrating its consistency and asymptotic normality, and applies it to improve covariance matrix estimation.

Contribution

A novel estimator for noise in spiked covariance models that is consistent, asymptotically normal, and minimax, enhancing covariance matrix estimation in high dimensions.

Findings

Estimator is strongly consistent

Estimator is asymptotically normal

Achieves minimax optimality

Abstract

The problem of estimating a spiked covariance matrix in high dimensions under Frobenius loss, and the parallel problem of estimating the noise in spiked PCA is investigated. We propose an estimator of the noise parameter by minimizing an unbiased estimator of the invariant Frobenius risk using calculus of variations. The resulting estimator is shown, using random matrix theory, to be strongly consistent and essentially asymptotically normal and minimax for the noise estimation problem. We apply the construction to construct a robust spiked covariance matrix estimator with consistent eigenvalues.