Graph selection with GGMselect

Christophe Giraud (1, 2) , Sylvie Huet (1) , Nicolas Verzelen (3)

1 Unité MIAJ - INRA - Mathématiques et Informatique Appliquées
2 CMAP - Centre de Mathématiques Appliquées - Ecole Polytechnique
3 LM-Orsay - Laboratoire de Mathématiques d'Orsay

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 models; Model selection; penalized empirical risk

Domains: Statistics [math.ST]; Statistics Theory [stat.TH]

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