Online Program Home
My Program

Abstract Details

Activity Number: 175 - Bayesian Theory, Foundations, and Nonparametrics
Type: Contributed
Date/Time: Monday, July 30, 2018 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #328382
Title: Bayesian Estimation Under Informative Sampling with Unattenuated Dependence
Author(s): Terrance Savitsky* and Matthew Williams
Companies: Bureau of Labor Statistics and SAMHSA/CBHSQ
Keywords: Cluster sampling; Stratification; Survey Sampling; Sampling Weights; Bayesian hierarchical models; MCMC

An informative sampling design leads to unit inclusion probabilities that are correlated with the response variable of interest. However, multistage sampling designs may also induce higher order dependencies, which are typically ignored in the literature when establishing consistency of estimators for survey data. We relax the condition of asymptotic independence or asymptotic factorization and demonstrate that consistency is still achieved in the presence of residual sampling dependence. A popular approach for conducting inference on a population based on a survey sample is the use of a pseudo-posterior, which uses first order sampling weights to exponentiate the likelihood. We show that the pseudo-posterior is consistent not only for survey designs which have asymptotic factorization, but also for designs with unattenuated dependence. Using the National Survey on Drug Use and Health, we explore the impact of multistage designs and order based sampling. The use of the survey-weighted pseudo-posterior together with our relaxed requirements for the survey design establish a broad class of analysis models that can be applied to a wide variety of survey data sets.

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

Back to the full JSM 2018 program