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March 2020 Bayesian Estimation Under Informative Sampling with Unattenuated Dependence
Matthew R. Williams, Terrance D. Savitsky
Bayesian Anal. 15(1): 57-77 (March 2020). DOI: 10.1214/18-BA1143


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 ignored in the literature when establishing consistency of estimators for survey data under a condition requiring asymptotic independence among the unit inclusion probabilities. This paper constructs new theoretical conditions that guarantee that the pseudo-posterior, which uses sampling weights based on first order inclusion probabilities to exponentiate the likelihood, is consistent not only for survey designs which have asymptotic factorization, but also for survey designs that induce residual or unattenuated dependence among sampled units. The use of the survey-weighted pseudo-posterior, together with our relaxed requirements for the survey design, establish a wide variety of analysis models that can be applied to a broad class of survey data sets. Using the complex sampling design of the National Survey on Drug Use and Health, we demonstrate our new theoretical result on multistage designs characterized by a cluster sampling step that expresses within-cluster dependence. We explore the impact of multistage designs and order based sampling.


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Matthew R. Williams. Terrance D. Savitsky. "Bayesian Estimation Under Informative Sampling with Unattenuated Dependence." Bayesian Anal. 15 (1) 57 - 77, March 2020.


Published: March 2020
First available in Project Euclid: 4 January 2019

zbMATH: 1437.62068
MathSciNet: MR4050877
Digital Object Identifier: 10.1214/18-BA1143

Primary: 62D05 , 62G20

Keywords: Cluster sampling , Markov chain Monte Carlo , sampling weights , stratification , survey sampling


Vol.15 • No. 1 • March 2020
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