iPACOSE: an iterative algorithm for the estimation of gene regulation networks

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

Abstract

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.

• Item Type: Paper (Technical Report)
• Form of publication: Preprint
• Keywords: Gaussian Graphical Models, high dimensional data, partial correlation estimation, covariance selection.
• Faculties: Mathematics, Computer Science and Statistics > Statistics > Technical Reports
• Subjects: 500 Science > 500 Science
• Language: English