Graph selection with GGMselect

Abstract

manuscrit HAL : hal-00401550, version 1 - 3 jul 2009Applications on inference of biological networks have raised a strong interest on the problem of graph estimation in high-dimensional Gaussian graphical model. 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. A nice behavior on numerical experiments corroborates these theoretical results. The procedure is implemented in the R-package GGMselect available onlin

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HAL-Polytechnique

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Last time updated on 08/06/2020

This paper was published in HAL-Polytechnique.

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