Abstract
Statistical estimates from survey samples have traditionally been obtained via design-based estimators. In many cases these estimators tend to work well for quantities, such as population totals or means, but can fall short as sample sizes become small. In today’s “information age,” there is a strong demand for more granular estimates. To meet this demand, using a Bayesian pseudolikelihood, we propose a computationally efficient unit-level modeling approach for non-Gaussian data collected under informative sampling designs. Specifically, we focus on binary and multinomial data. Our approach is both multivariate and multiscale, incorporating spatial dependence at the area level. We illustrate our approach through an empirical simulation study and through a motivating application to health insurance estimates, using the American Community Survey.
Funding Statement
Support for this research at the Missouri Research Data Center (MURDC) and through the Census Bureau Dissertation Fellowship program is gratefully acknowledged.
This research was partially supported by the U.S. National Science Foundation (NSF) under NSF Grant SES-1853096. This article is released to inform interested parties of ongoing research and to encourage discussion. The views expressed on statistical issues are those of the authors and not those of the NSF or U.S. Census Bureau. The DRB approval number for this paper is CBDRB-FY20-355.
Acknowledgments
We thank the Editor, Beth Ann Griffin, and two anonymous referees for valuable comments that helped improve this paper.
Citation
Paul A. Parker. Scott H. Holan. Ryan Janicki. "Computationally efficient Bayesian unit-level models for non-Gaussian data under informative sampling with application to estimation of health insurance coverage." Ann. Appl. Stat. 16 (2) 887 - 904, June 2022. https://doi.org/10.1214/21-AOAS1524
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