Online Program Home
My Program

Abstract Details

Activity Number: 46
Type: Invited
Date/Time: Sunday, July 31, 2016 : 4:00 PM to 5:50 PM
Sponsor: Survey Research Methods Section
Abstract #318258
Title: Scalable Bayes Under Informative Sampling
Author(s): Terrance Savitsky* and Sanvesh Srivastava
Companies: Bureau of Labor Statistics and University of Iowa
Keywords: Bayesian hierarchical models ; Survey sampling ; Barycenter ; Wasserstein distance ; Distributed Bayesian computations ; Pseudo posterior distribution

Bayesian models are increasingly employed by the U.S. Bureau of Labor Statistics (BLS) to render statistics, such as total employment, because these models readily account for structural dependencies in the data and estimate the full distribution from which variance estimates are computed. The estimation of posterior distributions is, however, computationally expensive. BLS collects data under informative sampling designs that assign probabilities of inclusion to be correlated with the response and induce a dependence among sampled observations. This article extends a computationally-scalable approach by composing the barycenter for a collection of pseudo posterior distributions estimated on disjoint subsets of the full data in the Wasserstein space. The extension generalizes the idea of stochastic approximation to calibrate uncertainty estimation on subset likelihoods by incorporating sampling weights. We construct conditions on known marginal and pairwise inclusion probabilities that define a class of sampling designs where consistency of the barycenter pseudo distribution is achieved. We demonstrate the result on an application to the Current Employment Statistics survey.

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

Back to the full JSM 2016 program

Copyright © American Statistical Association