Guillemot, Vincent; Bender, Andreas; Boulesteix, Anne-Laure
iPACOSE: an iterative algorithm for the estimation of gene regulation networks.
Department of Statistics: Technical Reports, No.133
In the context of Gaussian Graphical Models (GGMs) with high-
dimensional small sample data, we present a simple procedure to esti-
mate partial correlations under the constraint that some of them are
strictly zero. This method can also be extended to covariance selection.
If the goal is to estimate a GGM, our new procedure can be applied
to re-estimate the partial correlations after a first graph has been esti-
mated in the hope to improve the estimation of non-zero coefficients. In
a simulation study, we compare our new covariance selection procedure
to existing methods and show that the re-estimated partial correlation
coefficients may be closer to the real values in important cases.