High-dimensional covariance matrix estimation using a low-rank and diagonal decomposition

Abstract

We study high-dimensional covariance/precision matrix estimation under the assumption that the covariance/precision matrix can be decomposed into a low-rank component L and a diagonal component D. The rank of L can either be chosen to be small or controlled by a penalty function. Under moderate conditions on the population covariance/precision matrix itself and on the penalty function, we prove some consistency results for our estimators. A blockwise coordinate descent algorithm, which iteratively updates L and D, is then proposed to obtain the estimator in practice. Finally, various numerical experiments are presented: using simulated data, we show that our estimator performs quite well in terms of the Kullback-Leibler loss; using stock return data, we show that our method can be applied to obtain enhanced solutions to the Markowitz portfolio selection problem.

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• Latent variable graphical model selection via convex optimization
• High-dimensional covariance matrix estimation in approximate factor models
• High-dimensionality effects in the Markowitz problem and other quadratic programs with linear constraints: Risk underestimation