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.
Type de document :
Pré-publication, Document de travail
44 pages. 2012
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Contributeur : Christophe Giraud <>
Soumis le : mardi 14 février 2012 - 13:09:32
Dernière modification le : jeudi 10 mai 2018 - 02:03:57
Document(s) archivé(s) le : mardi 15 mai 2012 - 02:36:16


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



Christophe Giraud, Sylvie Huet, Nicolas Verzelen. Graph selection with GGMselect. 44 pages. 2012. 〈hal-00401550v2〉



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