Open Access
December 2013 Gaussian graphical model estimation with false discovery rate control
Weidong Liu
Ann. Statist. 41(6): 2948-2978 (December 2013). DOI: 10.1214/13-AOS1169


This paper studies the estimation of a high-dimensional Gaussian graphical model (GGM). Typically, the existing methods depend on regularization techniques. As a result, it is necessary to choose the regularized parameter. However, the precise relationship between the regularized parameter and the number of false edges in GGM estimation is unclear. In this paper we propose an alternative method by a multiple testing procedure. Based on our new test statistics for conditional dependence, we propose a simultaneous testing procedure for conditional dependence in GGM. Our method can control the false discovery rate (FDR) asymptotically. The numerical performance of the proposed method shows that our method works quite well.


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Weidong Liu. "Gaussian graphical model estimation with false discovery rate control." Ann. Statist. 41 (6) 2948 - 2978, December 2013.


Published: December 2013
First available in Project Euclid: 1 January 2014

zbMATH: 1288.62094
MathSciNet: MR3161453
Digital Object Identifier: 10.1214/13-AOS1169

Primary: 62H12 , 62H15

Keywords: False discovery rate , Gaussian graphical model , multiple tests

Rights: Copyright © 2013 Institute of Mathematical Statistics

Vol.41 • No. 6 • December 2013
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