  In high dimensions we propose and analyze an aggregation estimator of the
precision matrix for Gaussian graphical models. This estimator, called
graphical Exponential Screening (gES), linearly combines a suitable set of
individual estimators with different underlying graphs, and balances the
estimation error and sparsity. We study the risk of this aggregation estimator
and show that it is comparable to that of the best estimator based on a single
graph, chosen by an oracle. Numerical performance of our method is investigated
using both simulated and real datasets, in comparison with some state-of-art
estimation procedures.
