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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.
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Contributor : Christophe Giraud <>
Submitted on : Tuesday, February 14, 2012 - 1:09:32 PM
Last modification on : Friday, October 9, 2020 - 9:20:06 PM
Long-term archiving on: : Tuesday, May 15, 2012 - 2:36:16 AM


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



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



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