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

Key points

• For nonlinear models, equivalent weights can be defined locally through the sensitivity of a poststratified population estimator.1
• 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.1
• Simulations show that HGP achieves lower population-weighted prediction error under nonparametric structure, whereas logistic regression performs well under correct parametric specification.1
• 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.1

Sources (1)

• [1]Hierarchical Gaussian Processes for Survey Weighting: Local Equivalent Weights in Structured Populations
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 14, 03:08 PM

  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.

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