{
  "abstract": "Two features intrinsic to survey sampling complicate semiparametric efficiency analysis: design-induced dependence among sampling indicators and the randomness of finite-population targets under the superpopulation law. For general semiparametric full-data models, we show that the observed-data experiment under a broad class of dependent designs is locally asymptotically normal with the tangent-space structure of a reference Poisson experiment. Under the joint superpopulation-design law, first-order efficiency depends on the design only through the limiting inclusion-probability function. Standard missing-at-random projection in the reference experiment characterizes the observed-data efficient influence function. Finite-population targets are treated through first-order asymptotic expansions, extending the analysis beyond exact random sums to nonlinear census characteristics. Superpopulation and finite-population centerings yield equivalent notions of local regularity and efficiency, with their bounds linked by a Pythagorean decomposition that gives a generalized finite-population correction. We then give general and design-specific conditions under which cross-fitted estimators with estimated nuisance functions attain both efficiency bounds. For the finite-population mean, the bound equals the large-sample limit of the Godambe-Joshi anticipated-variance lower bound. For scalar targets, we characterize optimal limiting inclusion probabilities. Simulations and California Academic Performance Index data illustrate the theory.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.17288",
  "author_total": 2,
  "authors": [
    {
      "name": "Hiroki Chiba"
    },
    {
      "name": "Kosuke Morikawa"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6rhxn7xafvzr4.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.17288",
  "fulltext_url": null,
  "id": "6rhxn7xafvzr4",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.17288",
  "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",
      "math.ST"
    ],
    [
      "arxiv",
      "stat.ME"
    ],
    [
      "arxiv",
      "stat.TH"
    ],
    [
      "openalex",
      "2613|Survey Sampling and Estimation Techniques"
    ]
  ],
  "openalex": "W7213399802",
  "pdf_url": "https://arxiv.org/pdf/2609.17288",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-15",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/6rhxn7xafvzr4.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:21:45Z",
      "native_id": "2609.17288",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.17288"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.ST",
      "subfield": "statistics"
    }
  ],
  "title": "Semiparametric Efficient Inference under Non-Informative Complex Survey Designs",
  "title_text": "Semiparametric Efficient Inference under Non-Informative Complex Survey Designs",
  "type": "preprint",
  "updated_at": "2026-10-30T12:21:51Z",
  "url": "https://research.draconitic.com/paper/6rhxn7xafvzr4",
  "venue": "arXiv",
  "versions": []
}