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      Hybrid projection methods for large-scale inverse problems with mixed Gaussian priors

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      Inverse Problems

      IOP Publishing

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          Most cited references 35

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          De-noising by soft-thresholding

           D.L. Donoho (1995)
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            A well-conditioned estimator for large-dimensional covariance matrices

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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.
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                Author and article information

                Contributors
                Journal
                Inverse Problems
                Inverse Problems
                IOP Publishing
                0266-5611
                1361-6420
                March 08 2021
                April 01 2021
                March 08 2021
                April 01 2021
                : 37
                : 4
                : 044002
                Article
                10.1088/1361-6420/abd29d
                © 2021

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