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Hierarchical Gaussian Processes for Survey Weighting: Local Equivalent Weights in Structured Populations — arXiv:2610.20321

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.20321
License (as archived): CC0-1.0
Archived: 2026-10-15T14:24:23.708Z
SHA-256 of the archived text: 4846dde0d118920081d1228896e6244dd7faa6f68d3cf0eb27aa49ff88de8317
Stable URL of this page: https://metraipolis.com/documento/dc_be4f7ac8a563

How to cite

Hierarchical Gaussian Processes for Survey Weighting: Local Equivalent Weights in Structured Populations — arXiv:2610.20321 — arXiv (en), https://arxiv.org/abs/2610.20321 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_be4f7ac8a563 (Metraipolis archive dc_be4f7ac8a563, sha256 4846dde0d118920081d1228896e6244dd7faa6f68d3cf0eb27aa49ff88de8317)

BibTeX

@misc{dcbe4f7ac8a563,
  title = {Hierarchical Gaussian Processes for Survey Weighting: Local Equivalent Weights in Structured Populations — arXiv:2610.20321},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.20321},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_be4f7ac8a563; sha256 4846dde0d118920081d1228896e6244dd7faa6f68d3cf0eb27aa49ff88de8317},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.20321. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: statistica e scienza dei dati (elenco arXiv stat.ML, nuovi lavori del giorno).
Categorie arXiv: Methodology (stat.ME) ; Machine Learning (stat.ML).
Pagina del lavoro: https://arxiv.org/abs/2610.20321 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-15T13:54:20.323Z dalla pagina https://arxiv.org/list/stat.ML/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).