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Support Recovery of Gaussian Graphical Model with False Discovery Rate Control

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Abstract

This paper focuses on the support recovery of the Gaussian graphical model (GGM) with false discovery rate (FDR) control. The graceful symmetrized data aggregation (SDA) technique which involves sample splitting, data screening and information pooling is exploited via a node-based way. A matrix of test statistics with symmetry property is constructed and a data-driven threshold is chosen to control the FDR for the support recovery of GGM. The proposed method is shown to control the FDR asymptotically under some mild conditions. Extensive simulation studies and a real-data example demonstrate that it yields a better FDR control while offering reasonable power in most cases.

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Authors and Affiliations

  1. School of Mathematics and Statistics, Jiangsu Normal University, Xuzhou, 221116, China

    Yuhao Zhang

  2. School of Statistics and Data Science, Nankai University, Tianjin, 300071, China

    Yanhong Liu & Zhaojun Wang

Authors
  1. Yuhao Zhang
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  2. Yanhong Liu
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  3. Zhaojun Wang
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Corresponding author

Correspondence to Zhaojun Wang.

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The authors declare no conflict of interest.

Additional information

This research was supported partially by the China National Key R&D Program under Grant Nos. 2019YFC1908502, 2022YFA1003703, 2022YFA1003802, and 2022YFA1003803, and the National Natural Science Foundation of China under Grant Nos. 11925106, 12231011, 11931001, and 11971247.

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Cite this article

Zhang, Y., Liu, Y. & Wang, Z. Support Recovery of Gaussian Graphical Model with False Discovery Rate Control. J Syst Sci Complex 36, 2605–2623 (2023). https://doi.org/10.1007/s11424-023-2123-y

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  • DOI: https://doi.org/10.1007/s11424-023-2123-y

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