{
  "abstract": "Statistical modeling of high-dimensional and large-sample data with complex correlation structures is increasingly important in biomedical research, particularly in large-scale omics applications such as genomic studies. Analyzing these large-scale datasets imposes substantial computational and memory demands, requiring efficient model training strategies that maintain predictive accuracy. We propose Prescreen, Then Subsample (PTS), a two-stage approach that combines variable selection with optimal-design-based informative subsampling to address both high dimensionality and large sample size. The first stage screens informative variables and can explicitly account for covariate correlation; the second stage selects informative observations in the prescreened variable space. In contrast to existing two-stage subdata selection approaches, PTS can incorporate multivariable fine-mapping to identify a correlation-aware covariate space for informative subsampling. In particular, we integrate the Sum of Single Effects (SuSiE) model for fine-mapping-based prescreening with information-based optimal subdata selection (IBOSS) for sample selection. Simulation studies based on correlated Gaussian covariates and biobank-scale synthetic genotype data show that PTS reduces model training time while achieving predictive performance comparable to full-data training. We find that SuSiE-based PTS provides improved predictive performance consistent with better stage-one screening precision compared with commonly used univariate prescreening methods. PTS therefore provides a flexible and scalable training framework for large-scale biomedical data with complex correlation structures, including genomic data with linkage disequilibrium.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.21916",
  "author_total": 2,
  "authors": [
    {
      "name": "Subhadra Dasgupta"
    },
    {
      "name": "Christian Staerk"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/f3mk4x1hfepz4.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.21916",
  "fulltext_url": null,
  "id": "f3mk4x1hfepz4",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.21916",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "stat.ME"
    ],
    [
      "openalex",
      "2613|Statistical Methods and Inference"
    ]
  ],
  "openalex": "W7222703015",
  "pdf_url": "https://arxiv.org/pdf/2610.21916",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-15",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/f3mk4x1hfepz4.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:30:00Z",
      "native_id": "2610.21916",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.21916"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ME",
      "subfield": "statistics"
    }
  ],
  "title": "Prescreen, Then Subsample: Efficient model training for large-scale biomedical data with correlated covariates",
  "title_text": "Prescreen, Then Subsample: Efficient model training for large-scale biomedical data with correlated covariates",
  "type": "preprint",
  "updated_at": "2026-10-30T12:30:00Z",
  "url": "https://research.draconitic.com/paper/f3mk4x1hfepz4",
  "venue": "arXiv",
  "versions": []
}