期刊
STATISTICS & PROBABILITY LETTERS
卷 113, 期 -, 页码 41-48出版社
ELSEVIER
DOI: 10.1016/j.spl.2016.02.009
关键词
Biased sampling; Dirichlet process; Mixture model; Stratified sampling; Survey data
资金
- Nakajima Foundation
- National Institutes of Health [R01 ES017240, R01 ES020619]
- Eunice Kennedy Shriver National Institute of Child Health and Human Development
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. (C) 2016 Elsevier B.V. All rights reserved.
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