Covariance Estimation in High Dimensions via Kronecker Product Expansions

TL;DR

This paper introduces PRLS, a Kronecker product-based method for high-dimensional covariance estimation, demonstrating faster convergence and scalability, especially for low separation rank matrices, with applications in wind speed prediction.

Contribution

The paper proposes a novel permuted rank-penalized least-squares method leveraging Kronecker product expansions for scalable high-dimensional covariance estimation.

Findings

PRLS achieves faster convergence than sample covariance for low separation rank matrices.

The method provides a tradeoff framework between estimation and approximation errors.

Simulations and real data demonstrate the effectiveness of PRLS in wind speed prediction.

Abstract

This paper presents a new method for estimating high dimensional covariance matrices. The method, permuted rank-penalized least-squares (PRLS), is based on a Kronecker product series expansion of the true covariance matrix. Assuming an i.i.d. Gaussian random sample, we establish high dimensional rates of convergence to the true covariance as both the number of samples and the number of variables go to infinity. For covariance matrices of low separation rank, our results establish that PRLS has significantly faster convergence than the standard sample covariance matrix (SCM) estimator. The convergence rate captures a fundamental tradeoff between estimation error and approximation error, thus providing a scalable covariance estimation framework in terms of separation rank, similar to low rank approximation of covariance matrices. The MSE convergence rates generalize the high dimensional rates recently obtained for the ML Flip-flop algorithm for Kronecker product covariance estimation. We show that a class of block Toeplitz covariance matrices is approximatable by low separation rank and give bounds on the minimal separation rank $r$ that ensures a given level of bias. Simulations are presented to validate the theoretical bounds. As a real world application, we illustrate the utility of the proposed Kronecker covariance estimator for spatio-temporal linear least squares prediction of multivariate wind speed measurements.