Annals of Statistics

Gaussian graphical model estimation with false discovery rate control

Weidong Liu

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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.

Article information

Ann. Statist., Volume 41, Number 6 (2013), 2948-2978.

First available in Project Euclid: 1 January 2014

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Zentralblatt MATH identifier

Primary: 62H12: Estimation 62H15: Hypothesis testing

False discovery rate Gaussian graphical model multiple tests


Liu, Weidong. Gaussian graphical model estimation with false discovery rate control. Ann. Statist. 41 (2013), no. 6, 2948--2978. doi:10.1214/13-AOS1169.

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Supplemental materials

  • Supplementary material: Supplement to “Gaussian graphical model estimation with false discovery rate control”. This supplemental material includes additional numerical results for GFC-Dantizg and GFC-Lasso.