Conjugate Bayesian unit‐level modelling of count data under informative sampling designs

Unit‐level models for survey data offer many advantages over their area‐level counterparts, such as potential for more precise estimates and a natural benchmarking property. However, two main challenges occur in this context: accounting for an informative survey design and handling non‐Gaussian data...

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Bibliographic Details
Published in:Stat (International Statistical Institute) 2020, Vol.9 (1), p.n/a
Main Authors: Parker, Paul A., Holan, Scott H., Janicki, Ryan
Format: Article
Language:English
Subjects:
Bayesian
conjugate multivariate distribution
count data
pseudo‐likelihood
public use microdata
small area estimation
survey data
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Summary:Unit‐level models for survey data offer many advantages over their area‐level counterparts, such as potential for more precise estimates and a natural benchmarking property. However, two main challenges occur in this context: accounting for an informative survey design and handling non‐Gaussian data types. The pseudo‐likelihood approach is one solution to the former, and conjugate multivariate distribution theory offers a solution to the latter. By combining these approaches, we attain a unit‐level model for count data that accounts for informative sampling designs and includes a fully Bayesian model uncertainty propagation. Importantly, conjugate full conditional distributions hold under the pseudo‐likelihood, yielding an extremely computationally efficient approach. Our method is illustrated via an empirical simulation study using count data from the American Community Survey public use microdata sample.
ISSN:2049-1573
2049-1573
DOI:10.1002/sta4.267