{
  "abstract": "Regression-based survey weighting connects model-based population estimation with design-based inference through equivalent weights. For nonlinear models, equivalent weights can be defined locally through the sensitivity of a poststratified population estimator. However, conventional regression models may inadequately capture nonlinear relationships and complex interactions across sparse poststratification cells. We develop a survey-weighting framework based on hierarchical Gaussian process (HGP) regression, combining structured group-level effects with kernel-based smoothing to share information across related cells. We derive Gaussian posterior prediction formulas that exploit the structured covariance representation to reduce computational costs. We also establish a uniform convergence result for the posterior predictive mean under stated regularity conditions and construct local equivalent weights by matching the first-order sensitivity of a plug-in poststratified estimand to that of a weighted average of fitted response means, with both derivatives taken with respect to the latent mean-scale vector. Simulations show that HGP achieves lower population-weighted prediction error under nonparametric structure, whereas logistic regression performs well under correct parametric specification. Additional analyses examine sample size, frame coverage, subgroup prediction, and population-mean agreement. An application to the Future of Families and Child Wellbeing Study illustrates the method. Overall, this work extends local equivalent-weight construction to nonparametric modeling and provides a computational framework and conditional theoretical results for survey weighting in structured, sparse populations.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.20321",
  "author_total": 1,
  "authors": [
    {
      "name": "Seonghun Lee"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cph7x5381fa1m.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.20321",
  "fulltext_url": null,
  "id": "cph7x5381fa1m",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.20321",
  "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",
      "stat.ME"
    ],
    [
      "arxiv",
      "stat.ML"
    ],
    [
      "openalex",
      "2613|Survey Sampling and Estimation Techniques"
    ]
  ],
  "openalex": "W7222021021",
  "pdf_url": "https://arxiv.org/pdf/2610.20321",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-21",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/cph7x5381fa1m.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:34:29Z",
      "native_id": "2610.20321",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.20321"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ME",
      "subfield": "statistics"
    }
  ],
  "title": "Hierarchical Gaussian Processes for Survey Weighting: Local Equivalent Weights in Structured Populations",
  "title_text": "Hierarchical Gaussian Processes for Survey Weighting: Local Equivalent Weights in Structured Populations",
  "type": "preprint",
  "updated_at": "2026-10-30T12:34:29Z",
  "url": "https://research.draconitic.com/paper/cph7x5381fa1m",
  "venue": "arXiv",
  "versions": []
}