Bufan Li, Lujia Bai, Weichi Wu
arXiv 11 Dec 2025 · Statistics — Methodology
arXiv:2512.10467 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Benjamini, Yoav and Yekutieli, Daniel (2001) The control of the false discovery rate in multiple testing under dependency | 0.941 | 6 | 4 | 83% |
| 2 | Zhang, Xiao Lei and Begleiter, Henri and Porjesz, Bernice and Litke,… (1997) Electrophysiological evidence of memory impairment in alcoholic patients | 0.874 | 5 | 2 | 100% |
| 3 | Masuda, Naoki and Boyd, Zachary M and Garlaschelli, Diego and Mucha,… (2025) Introduction to correlation networks: Interdisciplinary approaches beyond thresholding | 0.737 | 3 | 2 | 100% |
| 4 | Bai, Lujia and Wu, Weichi (2025) Uniform variance reduced simultaneous inference of time-varying correlation networks self | 0.721 | 16 | 8 | 38% |
| 5 | Jia Chen and Degui Li and Yu-Ning Li and Oliver Linton (2025) Estimating time-varying networks for high-dimensional time series | 0.644 | 2 | 2 | 100% |
| 6 | Chang, Jinyuan and Chen, Xiaohui and Wu, Mingcong (2024) Central limit theorems for high dimensional dependent data | 0.644 | 2 | 2 | 100% |
| 7 | Zhao, Zhibiao (2015) Inference for local autocorrelations in locally stationary models | 0.644 | 2 | 2 | 100% |
| 8 | Wu, Weichi and Veitch, David and Zhou, Zhou (2024) Asynchronous Jump Testing and Estimation in High Dimensions Under Complex Temporal Dynamics self | 0.606 | 9 | 3 | 22% |
| 9 | Lurie, Daniel J and Kessler, Daniel and Bassett, Danielle S and Betz… (2020) Questions and controversies in the study of time-varying functional connectivity in resting fMRI | 0.585 | 3 | 1 | 100% |
| 10 | Xianyang Zhang and Guang Cheng (2018) Gaussian approximation for high dimensional vector under physical dependence | 0.511 | 3 | 2 | 33% |
Showing the top 10 of 35 scored citations.