Nonparametric Bayes modeling with sample survey weights.

Published

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Kunihama, T; Herring, AH; Halpern, CT; Dunson, DB

Published Date

  • June 2016

Published In

Volume / Issue

  • 113 /

Start / End Page

  • 41 - 48

PubMed ID

  • 31427835

Pubmed Central ID

  • 31427835

International Standard Serial Number (ISSN)

  • 0167-7152

Digital Object Identifier (DOI)

  • 10.1016/j.spl.2016.02.009

Language

  • eng