{
  "abstract": "We propose a heteroskedasticity-robust and dimention-adaptive test for multiple linear restric- tions in sparse high-dimensional linear regression models. It combines desparsified LASSO estimation with a ridge-regularized quadratic form statistic and critical values that adapt to the restriction dimension. Under stated regularity conditions, our approach is asymptotically valid under a fixed or diverging number of restrictions without specifying the asymptotic regime in advance. Our random-design theory allows independent non-identically distributed covari- ate rows with marginal sub-Weibull tails and heteroskedastic errors satisfying finite polynomial moments. Monte Carlo simulations demonstrate improved finite-sample size control relative to conventional Wald tests, particularly as the number of restrictions increases.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.16503",
  "author_total": 4,
  "authors": [
    {
      "name": "Dennis Lim"
    },
    {
      "name": "Pengjin Min"
    },
    {
      "name": "Jonathan H. W. Tan"
    },
    {
      "name": "Jiezheng Wei"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5y2nwga4919at.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.16503",
  "fulltext_url": null,
  "id": "5y2nwga4919at",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.16503",
  "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",
      "econ.EM"
    ],
    [
      "arxiv",
      "stat.ME"
    ],
    [
      "openalex",
      "2613|Statistical Methods and Inference"
    ]
  ],
  "openalex": "W7221835583",
  "pdf_url": "https://arxiv.org/pdf/2610.16503",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-12",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/5y2nwga4919at.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:26Z",
      "native_id": "2610.16503",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.16503"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ME",
      "subfield": "statistics"
    }
  ],
  "title": "Honest Inference for High-Dimensional Linear Models",
  "title_text": "Honest Inference for High-Dimensional Linear Models",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:26Z",
  "url": "https://research.draconitic.com/paper/5y2nwga4919at",
  "venue": "arXiv",
  "versions": []
}