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Learning Time-Varying Correlation Networks with FDR Control via Time-Varying P-values

Author

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  • Bufan Li
  • Lujia Bai
  • Weichi Wu

Abstract

This paper presents a systematic framework for controlling false discovery rate in learning time-varying correlation networks from high-dimensional, non-linear, non-Gaussian and non-stationary time series with an increasing number of potential abrupt change points in means. We propose a bootstrap-assisted approach to derive dependent and time-varying P-values from a robust estimate of time-varying correlation functions, which are not sensitive to change points. Our procedure is based on a new high-dimensional Gaussian approximation result for the uniform approximation of P-values across time and different coordinates. Moreover, we establish theoretically guaranteed Benjamini--Hochberg and Benjamini--Yekutieli procedures for the dependent and time-varying P-values, which can achieve uniform false discovery rate control. The proposed methods are supported by rigorous mathematical proofs and simulation studies. We also illustrate the real-world application of our framework using both brain electroencephalogram and financial time series data.

Suggested Citation

  • Bufan Li & Lujia Bai & Weichi Wu, 2025. "Learning Time-Varying Correlation Networks with FDR Control via Time-Varying P-values," Papers 2512.10467, arXiv.org, revised Dec 2025.
    • Handle: RePEc:arx:papers:2512.10467
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    References listed on IDEAS

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    1. Zhibiao Zhao, 2015. "Inference for Local Autocorrelations in Locally Stationary Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(2), pages 296-306, April.
    2. Bailey, Natalia & Pesaran, M. Hashem & Smith, L. Vanessa, 2019. "A multiple testing approach to the regularisation of large sample correlation matrices," Journal of Econometrics, Elsevier, vol. 208(2), pages 507-534.
    3. Diebold, Francis X. & Yılmaz, Kamil, 2014. "On the network topology of variance decompositions: Measuring the connectedness of financial firms," Journal of Econometrics, Elsevier, vol. 182(1), pages 119-134.
    4. Federica De Domenico & Giacomo Livan & Guido Montagna & Oreste Nicrosini, 2023. "Modeling and Simulation of Financial Returns under Non-Gaussian Distributions," Papers 2302.02769, arXiv.org.
    5. Chen, Jia & Li, Degui & Li, Yu-Ning & Linton, Oliver, 2025. "Estimating time-varying networks for high-dimensional time series," Journal of Econometrics, Elsevier, vol. 249(PC).
    6. T. Tony Cai & Weidong Liu, 2016. "Large-Scale Multiple Testing of Correlations," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(513), pages 229-240, March.
    7. Kazemilari, Mansooreh & Djauhari, Maman Abdurachman, 2015. "Correlation network analysis for multi-dimensional data in stocks market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 429(C), pages 62-75.
    8. De Domenico, Federica & Livan, Giacomo & Montagna, Guido & Nicrosini, Oreste, 2023. "Modeling and simulation of financial returns under non-Gaussian distributions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 622(C).
    9. Eric Jondeau & Ser-Huang Poon & Michael Rockinger, 2007. "Financial Modeling Under Non-Gaussian Distributions," Springer Finance, Springer, number 978-1-84628-696-4, March.
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