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

2026-09-20

AI Summary

本文提出了一种基于Copula散度的无模型特征筛选框架CD-Screen及控制假发现率的方法CD-FDR，有效解决了高维异构数据中特征筛选的问题。

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.

Problem

• model-free
• high-dimensional data
• heterogeneous data
• feature screening
• false discovery control

Innovation

• model-free
• distributionally robust dependence measure
• Copula Divergence
• false discovery control