Local Equivalent Weights
Within the hierarchical Gaussian process framework, weights derived by matching the first-order sensitivity of a model-assisted estimator to that of a weighted sample mean. They allow nonparametric estimators to be incorporated into classical survey inference, facilitating variance estimation and confidence interval construction.
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This study proposes a survey-weighting framework based on hierarchical Gaussian process (HGP) regression, using kernel smoothing to share information across related units, and derives local equivalent weights.
Local equivalent weights are constructed by matching the first-order sensitivity of the plug-in post-stratification estimator with the first-order sensitivity of a weighted average fitted response mean, both differentiated with respect to the latent mean scale vector.
This work extends the construction of local equivalent weights to nonparametric modeling, providing a computational framework and conditional theoretical results for survey weighting of structured, sparse populations.
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This study proposes a survey-weighting framework based on hierarchical Gaussian process (HGP) regression, using kernel smoothing to share information across related units, and derives local equivalent weights.
50%UnverifiedLocal equivalent weights are constructed by matching the first-order sensitivity of the plug-in post-stratification estimator with the first-order sensitivity of a weighted average fitted response mean, both differentiated with respect to the latent mean scale vector.
50%UnverifiedThis work extends the construction of local equivalent weights to nonparametric modeling, providing a computational framework and conditional theoretical results for survey weighting of structured, sparse populations.
50%