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Preprints, Working Papers, ... Year : 2012

Graph selection with GGMselect


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.
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Dates and versions

hal-00401550 , version 1 (03-07-2009)
hal-00401550 , version 2 (14-02-2012)



Christophe Giraud, Sylvie Huet, Nicolas Verzelen. Graph selection with GGMselect. 2012. ⟨hal-00401550v2⟩
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