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      A shrinkage approach to large-scale covariance matrix estimation and implications for functional genomics.

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          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.

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          Author and article information

          Journal
          Stat Appl Genet Mol Biol
          Statistical applications in genetics and molecular biology
          Walter de Gruyter GmbH
          1544-6115
          1544-6115
          2005
          : 4
          Affiliations
          [1 ] Department of Statistics, University of Munich, Germany. schaefer@stat.math.ethz.ch
          Article
          10.2202/1544-6115.1175
          16646851
          a1b6d34d-3643-4959-8ccc-c6e3a6e76c32
          History

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