Direct shrinkage estimation of large dimensional precision matrix

JOURNAL OF MULTIVARIATE ANALYSIS

卷:146

Article

Bodnar, Taras¹  Gupta, Arjun K.²  Parolya, Nestor³ 

[1] Stockholm Univ, Dept Math, Roslagsvagen 101, SE-10691 Stockholm, Sweden

[2] Bowling Green State Univ, Dept Math & Stat, Bowling Green, OH 43403 USA

[3] Leibniz Univ Hannover, Inst Empir Econ, D-30167 Hannover, Germany

关键词: Large-dimensional asymptotics;    Random matrix theory;    Precision matrix estimation;   

DOI  :  10.1016/j.jmva.2015.09.010

来源: Elsevier

【 摘 要 】

In this work we construct an optimal shrinkage estimator for the precision matrix in high dimensions. We consider the general asymptotics when the number of variables p -> infinity and the sample size n -> infinity so that p/n -> c is an element of (0, +infinity). The precision matrix is estimated directly, without inverting the corresponding estimator for the covariance matrix. The recent results from random matrix theory allow us to find the asymptotic deterministic equivalents of the optimal shrinkage intensities and estimate them consistently. The resulting distribution-free estimator has almost surely the minimum Frobenius loss. Additionally, we prove that the Frobenius norms of the inverse and of the pseudo-inverse sample covariance matrices tend almost surely to deterministic quantities and estimate them consistently. Using this result, we construct a bona fide optimal linear shrinkage estimator for the precision matrix in case c < 1. At the end, a simulation is provided where the suggested estimator is compared with the estimators proposed in the literature. The optimal shrinkage estimator shows significant improvement even for non-normally distributed data. (C) 2015 Elsevier Inc. All rights reserved.

【 授权许可】

Free