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High dimensional covariance matrix estimation

Lam, Clifford (2019) High dimensional covariance matrix estimation. Wiley Interdisciplinary Reviews: Computational Statistics. ISSN 1939-5108 (In Press)

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Abstract

Covariance matrix estimation plays an important role in statistical analysis in many elds, inclu- ding (but not limited to) portfolio allocation and risk management in nance, graphical modelling and clustering for genes discovery in bioinformatics, Kalman ltering and factor analysis in economics. In this paper, we give a selective review of covariance and precision matrix estimation when the matrix dimension can be diverging with, or even larger than the sample size. Two broad categories of regu- larization methods are presented. The rst category exploits an assumed structure of the covariance or precision matrix for consistent estimation. The second category shrinks the eigenvalues of a sam- ple covariance matrix, knowing from random matrix theory that such eigenvalues are biased from the population counterparts when the matrix dimension grows at the same rate as the sample size. Key

Item Type: Article
Additional Information: © 2019 Wiley Periodicals, Inc.
Divisions: Statistics
Subjects: H Social Sciences > HA Statistics
Date Deposited: 19 Sep 2019 11:30
Last Modified: 11 Oct 2019 23:11
URI: http://eprints.lse.ac.uk/id/eprint/101667

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