2 datasets found
  1. f

    Data from: Adaptive Selection for False Discovery Rate Control Leveraging...

    • tandf.figshare.com
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    Updated Jul 3, 2025
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    Kehan Wang; Yuexin Chen; Yixin Han; Wangli Xu; Linglong Kong (2025). Adaptive Selection for False Discovery Rate Control Leveraging Symmetry [Dataset]. http://doi.org/10.6084/m9.figshare.29473607.v1
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    zipAvailable download formats
    Dataset updated
    Jul 3, 2025
    Dataset provided by
    Taylor & Francis
    Authors
    Kehan Wang; Yuexin Chen; Yixin Han; Wangli Xu; Linglong Kong
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Controlling the false discovery rate (FDR) in high-dimensional multiple testing has recently been advanced through mirror statistics via knockoff and data splitting. However, these approaches primarily emphasize the symmetry structure of the one-dimensional mirror statistics while inadvertently overlooking the distribution information from non-null features when determining the rejection region, potentially causing a power loss. To tackle this challenge, we present a novel framework termed symmetry-based adaptive selection (SAS), which leverages the symmetry property of the two-dimensional statistics associated with the null features to estimate the local FDR and thereby determine the rejection region. We provide theoretical evidence for the asymptotic validity of FDR control and emphasize the superior power performance of our proposed SAS. Extensive numerical results from both synthetic experiments and two real-world datasets demonstrate that the proposed SAS achieves satisfactory FDR control and significant power improvements over existing methods.

  2. f

    IPAD: Stable Interpretable Forecasting with Knockoffs Inference

    • tandf.figshare.com
    zip
    Updated May 30, 2023
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    Yingying Fan; Jinchi Lv; Mahrad Sharifvaghefi; Yoshimasa Uematsu (2023). IPAD: Stable Interpretable Forecasting with Knockoffs Inference [Dataset]. http://doi.org/10.6084/m9.figshare.9619052.v2
    Explore at:
    zipAvailable download formats
    Dataset updated
    May 30, 2023
    Dataset provided by
    Taylor & Francis
    Authors
    Yingying Fan; Jinchi Lv; Mahrad Sharifvaghefi; Yoshimasa Uematsu
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Interpretability and stability are two important features that are desired in many contemporary big data applications arising in statistics, economics, and finance. While the former is enjoyed to some extent by many existing forecasting approaches, the latter in the sense of controlling the fraction of wrongly discovered features which can enhance greatly the interpretability is still largely underdeveloped. To this end, in this article, we exploit the general framework of model-X knockoffs introduced recently in Candès, Fan, Janson and Lv [(2018 ), “Panning for Gold: ‘model X’ Knockoffs for High Dimensional Controlled Variable Selection,” Journal of the Royal Statistical Society, Series B, 80, 551–577], which is nonconventional for reproducible large-scale inference in that the framework is completely free of the use of p-values for significance testing, and suggest a new method of intertwined probabilistic factors decoupling (IPAD) for stable interpretable forecasting with knockoffs inference in high-dimensional models. The recipe of the method is constructing the knockoff variables by assuming a latent factor model that is exploited widely in economics and finance for the association structure of covariates. Our method and work are distinct from the existing literature in which we estimate the covariate distribution from data instead of assuming that it is known when constructing the knockoff variables, our procedure does not require any sample splitting, we provide theoretical justifications on the asymptotic false discovery rate control, and the theory for the power analysis is also established. Several simulation examples and the real data analysis further demonstrate that the newly suggested method has appealing finite-sample performance with desired interpretability and stability compared to some popularly used forecasting methods. Supplementary materials for this article are available online.

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Share
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Email
Click to copy link
Link copied
Close
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Kehan Wang; Yuexin Chen; Yixin Han; Wangli Xu; Linglong Kong (2025). Adaptive Selection for False Discovery Rate Control Leveraging Symmetry [Dataset]. http://doi.org/10.6084/m9.figshare.29473607.v1

Data from: Adaptive Selection for False Discovery Rate Control Leveraging Symmetry

Related Article
Explore at:
zipAvailable download formats
Dataset updated
Jul 3, 2025
Dataset provided by
Taylor & Francis
Authors
Kehan Wang; Yuexin Chen; Yixin Han; Wangli Xu; Linglong Kong
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Description

Controlling the false discovery rate (FDR) in high-dimensional multiple testing has recently been advanced through mirror statistics via knockoff and data splitting. However, these approaches primarily emphasize the symmetry structure of the one-dimensional mirror statistics while inadvertently overlooking the distribution information from non-null features when determining the rejection region, potentially causing a power loss. To tackle this challenge, we present a novel framework termed symmetry-based adaptive selection (SAS), which leverages the symmetry property of the two-dimensional statistics associated with the null features to estimate the local FDR and thereby determine the rejection region. We provide theoretical evidence for the asymptotic validity of FDR control and emphasize the superior power performance of our proposed SAS. Extensive numerical results from both synthetic experiments and two real-world datasets demonstrate that the proposed SAS achieves satisfactory FDR control and significant power improvements over existing methods.

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