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Statistical Applications in Genetics and Molecular Biology

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Graph Selection with GGMselect

Christophe Giraud / Sylvie Huet / Nicolas Verzelen
Published Online: 2012-02-10 | DOI: https://doi.org/10.1515/1544-6115.1625

Applications on inference of biological networks have raised a strong interest in the problem of graph estimation in high-dimensional Gaussian graphical models. To handle this problem, we propose a two-stage procedure which first builds a family of candidate graphs from the data, and then selects one graph among this family according to a dedicated criterion. This estimation procedure is shown to be consistent in a high-dimensional setting, and its risk is controlled by a non-asymptotic oracle-like inequality. The procedure is tested on a real data set concerning gene expression data, and its performances are assessed on the basis of a large numerical study.The procedure is implemented in the R-package GGMselect available on the CRAN.

Keywords: Gaussian graphical model; model selection; penalized empirical risk

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Published Online: 2012-02-10

Citation Information: Statistical Applications in Genetics and Molecular Biology, ISSN (Online) 1544-6115, DOI: https://doi.org/10.1515/1544-6115.1625.

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©2012 Walter de Gruyter GmbH & Co. KG, Berlin/Boston. Copyright Clearance Center

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