Jump to ContentJump to Main Navigation
Show Summary Details
In This Section

Statistical Applications in Genetics and Molecular Biology

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

6 Issues per year


IMPACT FACTOR 2016: 0.646
5-year IMPACT FACTOR: 1.191

CiteScore 2016: 0.94

SCImago Journal Rank (SJR) 2015: 0.954
Source Normalized Impact per Paper (SNIP) 2015: 0.554

Mathematical Citation Quotient (MCQ) 2015: 0.06

Online
ISSN
1544-6115
See all formats and pricing
In This Section

Graph Selection with GGMselect

Christophe Giraud
  • Ecole Polytechnique
/ Sylvie Huet
  • Institut National de la Recherche Agronomique
/ Nicolas Verzelen
  • Institut National de la Recherche Agronomique
Published Online: 2012-02-10 | DOI: https://doi.org/10.1515/1544-6115.1625

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

About the article

Published Online: 2012-02-10



Citation Information: Statistical Applications in Genetics and Molecular Biology, ISSN (Online) 1544-6115, DOI: https://doi.org/10.1515/1544-6115.1625. Export Citation

Comments (0)

Please log in or register to comment.
Log in