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

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.
Year of publication:
2007
Authors: Juliane Schäfer ; Strimmer, Korbinian
Published in:
Statistical Applications in Genetics and Molecular Biology. - Berkeley Electronic Press. - Vol. 4.2007, 1, p. 32-32
Publisher: Berkeley Electronic Press
Subject: Shrinkage | covariance estimation | "small n | large p" problem | graphical Gaussian model (GGM) | genetic network | gene expression
Extent:
application/pdf
Type of publication: Article
Notes:
oai:bepress:
Source:
Persistent link: https://ebvufind01.dmz1.zbw.eu/10005046623
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