{
  "abstract": "Prediction-powered inference (PPI) combines full-frame predictions with a small labelled sample to estimate finite-population regression coefficients. We establish conditions for the conditional validity of existing calibrated residual-regression PPI estimators under nonuniform label acquisition and spatially dependent prediction errors. Approximate calibration can leave a population-specific displacement: the projection of prediction errors onto coefficient-specific calibration defects. We characterize its role in conditional inference through the noncentrality of a fixed-candidate Wald region. The relevant target-weight function is the coefficient loading divided by acquisition intensity. When mean-approximation error does not dominate, directional covariance control yields a wider sufficient recovery regime than a total residual-energy bound. A bounded spectral-basis theorem gives conditional coverage under directional covariance, tail and nondegeneracy conditions, with Gaussian and bounded non-Gaussian spatial examples. The analysis accounts for estimation of the one-step augmentation and permits same-label selection from a fixed finite library with a common first-order influence vector. A periodic Fourier construction attains the stochastic directional-gap order within a restricted dictionary class. Constructed and real-record-set experiments illustrate why mean calibration need not protect a slope. The results concern descriptive census projection coefficients under the stated sampling and field conditions.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2608.10356",
  "author_total": 1,
  "authors": [
    {
      "name": "Shinichiro Shirota"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/95tx49hgbkb6r.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2608.10356",
  "fulltext_url": null,
  "id": "95tx49hgbkb6r",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2608.10356",
  "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|Survey Sampling and Estimation Techniques"
    ]
  ],
  "openalex": "W7202247532",
  "pdf_url": "https://arxiv.org/pdf/2608.10356",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-13",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/95tx49hgbkb6r.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:26Z",
      "native_id": "2608.10356",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2608.10356"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ME",
      "subfield": "statistics"
    }
  ],
  "title": "Conditional validity of prediction-powered inference for spatially dependent finite populations",
  "title_text": "Conditional validity of prediction-powered inference for spatially dependent finite populations",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:26Z",
  "url": "https://research.draconitic.com/paper/95tx49hgbkb6r",
  "venue": "arXiv",
  "versions": []
}