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September 2015 Bayesian Nonparametric Weighted Sampling Inference
Yajuan Si, Natesh S. Pillai, Andrew Gelman
Bayesian Anal. 10(3): 605-625 (September 2015). DOI: 10.1214/14-BA924


It has historically been a challenge to perform Bayesian inference in a design-based survey context. The present paper develops a Bayesian model for sampling inference in the presence of inverse-probability weights. We use a hierarchical approach in which we model the distribution of the weights of the nonsampled units in the population and simultaneously include them as predictors in a nonparametric Gaussian process regression. We use simulation studies to evaluate the performance of our procedure and compare it to the classical design-based estimator. We apply our method to the Fragile Family and Child Wellbeing Study. Our studies find the Bayesian nonparametric finite population estimator to be more robust than the classical design-based estimator without loss in efficiency, which works because we induce regularization for small cells and thus this is a way of automatically smoothing the highly variable weights.


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Yajuan Si. Natesh S. Pillai. Andrew Gelman. "Bayesian Nonparametric Weighted Sampling Inference." Bayesian Anal. 10 (3) 605 - 625, September 2015.


Published: September 2015
First available in Project Euclid: 2 February 2015

zbMATH: 1334.62024
MathSciNet: MR3420817
Digital Object Identifier: 10.1214/14-BA924

Keywords: Gaussian process prior , model-based survey inference , poststratification , Stan , survey weighting

Rights: Copyright © 2015 International Society for Bayesian Analysis


Vol.10 • No. 3 • September 2015
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