Jump to ContentJump to Main Navigation

Statistical Applications in Genetics and Molecular Biology

Editor-in-Chief: Stumpf, Michael P.H.

6 Issues per year

Increased IMPACT FACTOR 2012: 1.717
Rank 18 out of 117 in category Statistics & Probability in the 2012 Thomson Reuters Journal Citation Report/Science Edition
Mathematical Citation Quotient 2012: 0.07

VolumeIssuePage

Graph Selection with GGMselect

Christophe Giraud1 / Sylvie Huet2 / Nicolas Verzelen3

1Ecole Polytechnique

2Institut National de la Recherche Agronomique

3Institut National de la Recherche Agronomique

Citation Information: Statistical Applications in Genetics and Molecular Biology. Volume 11, Issue 3, ISSN (Online) 1544-6115, DOI: 10.1515/1544-6115.1625, February 2012

Publication History

Published Online:
2012-02-10

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

Comments (0)

Please log in or register to comment.
Users without a subscription are not able to see the full content. Please, subscribe or login to access all content.