Nonparametric Bayes modeling with sample survey weights

Citation

Kunihama, T.; Herring, A. H.; Halpern, Carolyn Tucker; & Dunson, D. B. (2016). Nonparametric Bayes modeling with sample survey weights. Statistics and Probability Letters. vol. 113 pp. 41-48

Abstract

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.

URL

http://dx.doi.org/10.1016/j.spl.2016.02.009

Keyword(s)

Biased sampling Dirichlet process Mixture model Stratified sampling Survey data

Reference Type

Journal Article

Journal Title

Statistics and Probability Letters

Author(s)

Kunihama, T.
Herring, A. H.
Halpern, Carolyn Tucker
Dunson, D. B.

Year Published

2016

Volume Number

113

Pages

41-48

Edition

March 4, 2016

DOI

10.1016/j.spl.2016.02.009

Reference ID

7897