{
  "abstract": "We develop a framework for conformal prediction in dyadic regression problems under complex missingness mechanisms. At the theoretical level, we develop general technical tools for establishing finite-sample validity of conformal prediction under distributional invariance conditions weaker than exchangeability. A key result handles the case where the sample itself is a random subset of the index set, a setting not covered by existing theory. In addition, we propose conformal prediction procedures for jointly exchangeable arrays, including full conformal, split conformal, a row-column approach exploiting similarities within rows and columns, and a selective conformal procedure achieving mask-conditional validity. For missing elements, we establish asymptotic validity of a weighted conformal procedure under a nonparametric graphon model for the missingness mechanism. We further establish conditional validity results for both continuous and discrete responses; to the best of our knowledge, this is the first formal proof of asymptotic conditional validity for weighted conformal prediction under a missing-not-at-random assumption. The proposed methods are illustrated on synthetic and real network data.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2606.11136",
  "author_total": 4,
  "authors": [
    {
      "name": "Robert Lunde"
    },
    {
      "name": "Minjie Yang"
    },
    {
      "name": "Elizaveta Levina"
    },
    {
      "name": "Ji Zhu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6zn8wgrzq1p5a.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2606.11136",
  "fulltext_url": null,
  "id": "6zn8wgrzq1p5a",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2606.11136",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
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  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "math.ST"
    ],
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      "arxiv",
      "stat.ME"
    ],
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      "arxiv",
      "stat.ML"
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      "arxiv",
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      "openalex",
      "2613|Statistical Methods and Inference"
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  "openalex": "W7164187423",
  "pdf_url": "https://arxiv.org/pdf/2606.11136",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-16",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/6zn8wgrzq1p5a.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:22:04Z",
      "native_id": "2606.11136",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2606.11136"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.ST",
      "subfield": "statistics"
    }
  ],
  "title": "Conformal Prediction for Dyadic Regression Under Complex Missingness",
  "title_text": "Conformal Prediction for Dyadic Regression Under Complex Missingness",
  "type": "preprint",
  "updated_at": "2026-10-30T12:22:10Z",
  "url": "https://research.draconitic.com/paper/6zn8wgrzq1p5a",
  "venue": "arXiv",
  "versions": []
}