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Activity Number: 46
Type: Invited
Date/Time: Sunday, July 31, 2016 : 4:00 PM to 5:50 PM
Sponsor: Survey Research Methods Section
Abstract #318089 View Presentation
Title: Nonparametric Bayes Modeling with Sample Survey Weights
Author(s): Tsuyoshi Kunihama* and Amy Herring and Carolyn Halpern and David Dunson
Companies: University of Washington and The University of North Carolina at Chapel Hill and The University of North Carolina at Chapel Hill and Duke University
Keywords: Biased sampling ; Dirichlet process ; Mixture model ; Stratified sampling

In population studies, it is standard to sample data via designs in which the population is divided into strata, with the different strata assigned different probabilities of inclusion. Although there have been some proposals for including sample survey weights into Bayesian analyses, existing methods require complex models or ignore the stratified design underlying the survey weights. We propose a simple approach based on modeling the distribution of the selected sample as a mixture, with the mixture weights appropriately adjusted, while accounting for uncertainty in the adjustment. We focus for simplicity on Dirichlet process mixtures but the proposed approach can be applied more broadly. We sketch a simple Markov chain Monte Carlo algorithm for computation, and assess the approach via simulations and an application.

Authors who are presenting talks have a * after their name.

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