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Activity Number: 499
Type: Contributed
Date/Time: Wednesday, August 7, 2013 : 8:30 AM to 10:20 AM
Sponsor: Survey Research Methods Section
Abstract - #309213
Title: Bayesian Nonparametric Finite Population Inference
Author(s): Yajuan Si*+ and Natesh S. Pillai and Andrew Gelman
Companies: Columbia University and Harvard University and Columbia University
Keywords: Survey Weighting ; Bayesian ; Gaussian Process ; Population Inference ; Model-based

Survey weighting adjustment takes the commonly used unequal sampling design, coverage discrepancy and nonresponse propensity into account and makes the sample be more representative of the target population. The weights are constructed on the variables that affect the probability of inclusion of samples to facilitate the finite population inference. Model-based inference predicts values of survey variables in the non-sampled units by including the survey design as model predictors. Since data analysts and survey organizers have different working duties, the analysts are often unclear about weights construction procedure. In this paper, we assume that we do not have any information on the predictors used in weighting. It is the weights themselves that implicitly contain all the information used in weighting. We propose a Bayesian procedure to account for the weights' uncertainty and include them as predictors for a nonparametric regression model under Gaussian process prior to make inference for the underlying finite population. We use simulation studies to evaluate the performance of the Bayesian procedure and apply it to the Fragile Families study.

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