{
  "abstract": "Conditional independence involving more than two variables, or multi-way conditional independence, is a fundamental probabilistic structure useful in many areas of statistical research, including latent-variable and graphical models. However, most existing works focus on pairwise conditional independence. To address this gap, we propose a kernel-based test of multi-way conditional independence. Specifically, we characterize this hypothesis through the simultaneous vanishing of a vector of conditional cross-covariance operators and construct a test statistic based on the norm of the vector of their estimators. We derive the statistic's asymptotic distributions under the null hypothesis and local alternatives. As a related methodological contribution, we also develop a test of multi-way unconditional independence and provide a procedure for its implementation. An application to RNA data illustrates the method's use in genomics.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2611.01092",
  "author_total": 3,
  "authors": [
    {
      "name": "Haoning Chen"
    },
    {
      "name": "Yin Tang"
    },
    {
      "name": "Bing Li"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1fyf1yzfmny7y.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2611.01092",
  "fulltext_url": null,
  "id": "1fyf1yzfmny7y",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2611.01092",
  "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": null,
  "pdf_url": "https://arxiv.org/pdf/2611.01092",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/1fyf1yzfmny7y.ris",
  "sources": [
    {
      "first_seen": "2026-11-03T12:21:21Z",
      "native_id": "2611.01092",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2611.01092"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ME",
      "subfield": "statistics"
    }
  ],
  "title": "A Kernel-Based Test for Multi-Way Conditional Independence",
  "title_text": "A Kernel-Based Test for Multi-Way Conditional Independence",
  "type": "preprint",
  "updated_at": "2026-11-05T15:20:35Z",
  "url": "https://research.draconitic.com/paper/1fyf1yzfmny7y",
  "venue": "arXiv",
  "versions": []
}