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.
Document type :
Preprints, Working Papers, ...
Complete list of metadatas

https://hal.archives-ouvertes.fr/hal-00401550
Contributor : Christophe Giraud <>
Submitted on : Tuesday, February 14, 2012 - 1:09:32 PM
Last modification on : Wednesday, March 27, 2019 - 4:08:31 PM
Long-term archiving on : Tuesday, May 15, 2012 - 2:36:16 AM

Files

GGMselect-arxiv.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-00401550, version 2
  • ARXIV : 0907.0619

Collections

Citation

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

Share

Metrics

Record views

346

Files downloads

378