Graph Selection with GGMselect

Giraud Christophe, Huet Sylvie and Verzelen Nicolas
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Giraud Christophe: Ecole Polytechnique
Huet Sylvie: Institut National de la Recherche Agronomique
Verzelen Nicolas: Institut National de la Recherche Agronomique

Statistical Applications in Genetics and Molecular Biology, 2012, vol. 11, issue 3, 52

Abstract: 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 (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (2)

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DOI: 10.1515/1544-6115.1625

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