Model-free and Distributionally Robust Feature Screening with False Discovery Control for High-Dimensional Heterogeneous Data

Cong Cheng; Runze Li; Yuan Ke

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arXiv · 2609.23635

Model-free and Distributionally Robust Feature Screening with False Discovery Control for High-Dimensional Heterogeneous Data

Cong Cheng·Runze Li·Yuan Ke

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Abstract

In this paper, we propose a model-free feature screening framework tailored for high-dimensional and heterogeneous datasets, based on a novel distributionally robust dependence measure termed Copula Divergence. The proposed screening method, named CD-Screen, addresses critical limitations of existing feature screening methods, such as restrictive modeling assumptions and sensitivity to heterogeneous feature distributions. CD-Screen ranks features according to their Copula Divergence without relying on a specific regression model or distributional assumptions. Additionally, we introduce CD-FDR, a data-driven procedure to control false discoveries, ensuring accurate and efficient feature selection. Theoretical analyses establish the sure screening and rank consistency properties of CD-Screen, along with asymptotic control of the false discovery rate by CD-FDR. Extensive simulation studies demonstrate the superior performance of our methods compared to traditional screening approaches across diverse scenarios. Furthermore, a real data analysis of the relationship between stock returns and inflation in the United States {illustrates the practical use of our method and provides descriptive evidence on} sector-specific responses to economic changes.

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BibTeX RIS

Cong Cheng, Runze Li, Yuan Ke. 2026-09-20. Model-free and Distributionally Robust Feature Screening with False Discovery Control for High-Dimensional Heterogeneous Data. https://arxiv.org/abs/2609.23635

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