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arXiv · 0907.0619

Graph selection with GGMselect

Christophe Giraud·Sylvie Huet·Nicolas Verzelen
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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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Christophe Giraud, Sylvie Huet, Nicolas Verzelen. 2012-02-15. Graph selection with GGMselect. https://arxiv.org/abs/0907.0619

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