Abstract Details
Activity Number:
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355
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Type:
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Contributed
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Date/Time:
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Tuesday, August 5, 2014 : 11:35 AM to 12:20 PM
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Sponsor:
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Survey Research Methods Section
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Abstract #314048
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Title:
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Weight Smoothing Using Laplace Priors
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Author(s):
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Michael Elliott*+ and Xi Xia
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Companies:
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University of Michigan and University of Michigan
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Keywords:
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Survey Methods ;
Windsorization ;
Weight Trimming ;
Bayesian finite population models
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Abstract:
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When analyzing data sampled with unequal inclusion probabilities, correlations between the probability of selection and the sampled data can induce bias. Weights equal to the inverse of the probability of selection are commonly used to correct this possible bias. When weights are uncorrelated with the sampled data, or more specifically the descriptive or model estimators of interest, highly disproportional sample design resulting in large weights can introduce unnecessary variability, leading to an overall larger root mean square error (RMSE) comparing to the unweighted or Winsorized methods.
We describe an approach we term weight smoothing that models the interactions between the weights and the estimators of interest as random effects, reducing the overall RMSE by shrinking interactions toward zero when such shrinkage is supported by data. Here we adapt a more flexible Laplace prior distribution for the hierarchical Bayesian model in order to gain more robustness against model misspecification. Simulations and applications are explored.
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Authors who are presenting talks have a * after their name.
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