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A Shrinkage Approach to Large-Scale Covariance Matrix Estimation and Implications for Functional Genomics

A Shrinkage Approach to Large-Scale Covariance Matrix Estimation and Implications for Functional... Inferring large-scale covariance matrices from sparse genomic data is an ubiquitous problem in bioinformatics. Clearly, the widely used standard covariance and correlation estimators are ill-suited for this purpose. As statistically efficient and computationally fast alternative we propose a novel shrinkage covariance estimator that exploits the Ledoit-Wolf (2003) lemma for analytic calculation of the optimal shrinkage intensity.Subsequently, we apply this improved covariance estimator (which has guaranteed minimum mean squared error, is well-conditioned, and is always positive definite even for small sample sizes) to the problem of inferring large-scale gene association networks. We show that it performs very favorably compared to competing approaches both in simulations as well as in application to real expression data. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Statistical Applications in Genetics and Molecular Biology de Gruyter

A Shrinkage Approach to Large-Scale Covariance Matrix Estimation and Implications for Functional Genomics

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References (23)

Publisher
de Gruyter
Copyright
Copyright © 2005 by the
ISSN
2194-6302
eISSN
1544-6115
DOI
10.2202/1544-6115.1175
pmid
16646851
Publisher site
See Article on Publisher Site

Abstract

Inferring large-scale covariance matrices from sparse genomic data is an ubiquitous problem in bioinformatics. Clearly, the widely used standard covariance and correlation estimators are ill-suited for this purpose. As statistically efficient and computationally fast alternative we propose a novel shrinkage covariance estimator that exploits the Ledoit-Wolf (2003) lemma for analytic calculation of the optimal shrinkage intensity.Subsequently, we apply this improved covariance estimator (which has guaranteed minimum mean squared error, is well-conditioned, and is always positive definite even for small sample sizes) to the problem of inferring large-scale gene association networks. We show that it performs very favorably compared to competing approaches both in simulations as well as in application to real expression data.

Journal

Statistical Applications in Genetics and Molecular Biologyde Gruyter

Published: Nov 14, 2005

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