{
  "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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.23635",
  "author_total": 3,
  "authors": [
    {
      "name": "Cong Cheng"
    },
    {
      "name": "Runze Li"
    },
    {
      "name": "Yuan Ke"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ayap0j07ajbhg.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.23635",
  "fulltext_url": null,
  "id": "ayap0j07ajbhg",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.23635",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "stat.ME"
    ],
    [
      "openalex",
      "2613|Statistical Methods and Inference"
    ]
  ],
  "openalex": "W7214011021",
  "pdf_url": "https://arxiv.org/pdf/2609.23635",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-20",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/ayap0j07ajbhg.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:23:33Z",
      "native_id": "2609.23635",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.23635"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ME",
      "subfield": "statistics"
    }
  ],
  "title": "Model-free and Distributionally Robust Feature Screening with False Discovery Control for High-Dimensional Heterogeneous Data",
  "title_text": "Model-free and Distributionally Robust Feature Screening with False Discovery Control for High-Dimensional Heterogeneous Data",
  "type": "preprint",
  "updated_at": "2026-10-30T12:23:33Z",
  "url": "https://research.draconitic.com/paper/ayap0j07ajbhg",
  "venue": "arXiv",
  "versions": []
}