Online Program Home
My Program

Abstract Details

Activity Number: 215 - Evolving Survey Inference in the Big Data Era: Challenges and Opportunities
Type: Invited
Date/Time: Monday, July 29, 2019 : 2:00 PM to 3:50 PM
Sponsor: Survey Research Methods Section
Abstract #300020 Presentation
Title: Novel Methods for Incorporating Sample Designs in Bayesian Inference
Author(s): Michael Elliott* and Yuqi Zhai and Trivellore Raghunathan
Companies: University of Michigan and University of Michigan and University of Michigan
Keywords: Survey sampling; importance sampling; small-area estimation; Bayesian bootstrap

Blending the Bayesian paradigm, with its emphasis on complex modeling, with the survey sampling paradigm, with its emphasis on non-parametrics and robustness, has been difficult. Particularly problematic has been incorporating weights into analysis, which, depending on the setting, can be either unnecessary, helpful to avoid magnifying model misspecification, or required if sampling is informative. This talk outlines methods to use recently developed methodology for incorporating complex sample designs in a weighted finite population Bayesian bootstrap procedure (Dong et al. 2014; Zhou et al. 2016) to incorporate design effects into Bayesian analyses via importance weighting. We consider this approach in a few simulation settings, and discuss applications to accounting for complex sample design in the setting of small area estimation.

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

Back to the full JSM 2019 program