Work

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

Year: 2020

Type: article

Abstract: 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.

Source: Stat

DOI: 10.1002/sta4.267

Authors: Paul A. Parker, Scott H. Holan, Ryan Janicki

Institutions: University of Missouri, United States Census Bureau

Language: English

Topic: Survey Sampling and Estimation Techniques

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, U.S. Census Bureau

Awards: NCRN-MN: Improving the Interpretability and Usability of the American Community Survey Through Hierarchical Multiscale Spatio-Temporal Statistical Models, G8805029320