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Guillemot, Vincent; Bender, Andreas; Boulesteix, Anne-Laure (December 2012): 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.