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Direct shrinkage estimation of large dimensional precisionmatrix
Stockholm University, Faculty of Science, Department of Mathematics.
2016 (English)In: Journal of Multivariate Analysis, ISSN 0047-259X, E-ISSN 1095-7243, Vol. 146, 223-236 p.Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2016. Vol. 146, 223-236 p.
Keyword [en]
Large-dimensional asymptotics, Random matrix theory, Precision matrix estimation
National Category
Probability Theory and Statistics
Research subject
Statistics
Identifiers
URN: urn:nbn:se:su:diva-128383DOI: 10.1016/j.jmva.2015.09.010ISI: 000373648200018OAI: oai:DiVA.org:su-128383DiVA: diva2:914677
Available from: 2016-03-24 Created: 2016-03-24 Last updated: 2016-05-09Bibliographically approved

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Bodnar, Taras
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ReferencesLink to record
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