Estimation of sparse covariance matrix via non-convex regularization

Xin Wang, Lingchen Kong and Liqun Wang

Journal of Multivariate Analysis, 2024, vol. 202, issue C

Abstract: Estimation of high-dimensional sparse covariance matrix is one of the fundamental and important problems in multivariate analysis and has a wide range of applications in many fields. This paper presents a novel method for sparse covariance matrix estimation via solving a non-convex regularization optimization problem. We establish the asymptotic properties of the proposed estimator and develop a multi-stage convex relaxation method to find an effective estimator. The multi-stage convex relaxation method guarantees any accumulation point of the sequence generated is a first-order stationary point of the non-convex optimization. Moreover, the error bounds of the first two stage estimators of the multi-stage convex relaxation method are derived under some regularity conditions. The numerical results show that our estimator outperforms the state-of-the-art estimators and has a high degree of sparsity on the premise of its effectiveness.

Keywords: Multi-stage convex relaxation method; Non-convex regularization; Sparse covariance matrix (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1016/j.jmva.2024.105294

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