title: Graph selection with GGMselect.
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.
authors.affiliation: Ecole Polytechnique.
authors.forename: Christophe
authors.initials: C
authors.lastname: Giraud
authors.forename: Sylvie
authors.initials: S
authors.lastname: Huet
authors.forename: Nicolas
authors.initials: N
authors.lastname: Verzelen
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods; D003198:Computer Simulation; D015233:Models, Statistical; D015203:Reproducibility of Results; D012984:Software