Estimation of the covariance structure of heavy-tailed distributions

TL;DR

This paper introduces a new covariance matrix estimator suitable for heavy-tailed distributions, providing strong theoretical guarantees under weak assumptions and applicable in high-dimensional settings.

Contribution

It proposes a novel covariance estimator with proven deviation bounds that depend on intrinsic dimension, advancing analysis for heavy-tailed data.

Findings

Provides tight deviation inequalities for the estimator

Applicable to high-dimensional data with weak moment assumptions

Addresses covariance estimation challenges in heavy-tailed distributions

Abstract

We propose and analyze a new estimator of the covariance matrix that admits strong theoretical guarantees under weak assumptions on the underlying distribution, such as existence of moments of only low order. While estimation of covariance matrices corresponding to sub-Gaussian distributions is well-understood, much less in known in the case of heavy-tailed data. As K. Balasubramanian and M. Yuan write, "data from real-world experiments oftentimes tend to be corrupted with outliers and/or exhibit heavy tails. In such cases, it is not clear that those covariance matrix estimators .. remain optimal" and "..what are the other possible strategies to deal with heavy tailed distributions warrant further studies." We make a step towards answering this question and prove tight deviation inequalities for the proposed estimator that depend only on the parameters controlling the "intrinsic dimension" associated to the covariance matrix (as opposed to the dimension of the ambient space); in particular, our results are applicable in the case of high-dimensional observations.