Abstract
Leveraging multivariate spatial dependence to improve the precision of estimates using American Community Survey data and other sample survey data has been a topic of recent interest among data users and federal statistical agencies. One strategy is to use a multivariate spatial mixed effects model with a Gaussian observation model and latent Gaussian process model. In practice, this works well for a wide range of tabulations. Nevertheless, in situations in which the data exhibit heterogeneity within or across geographies, and/or there is sparsity in the data, the Gaussian assumptions may be problematic and lead to underperformance. To remedy these situations, we propose a multivariate hierarchical Bayesian nonparametric mixed effects spatial mixture model to increase model flexibility. The number of clusters is chosen automatically in a data-driven manner. The effectiveness of our approach is demonstrated through a simulation study and motivating application of special tabulations for American Community Survey data.
Acknowledgments
The DRB approval number for this paper is CDBRB-FY20-044. This report is released to inform interested parties of ongoing research and to encourage discussion of work in progress. The views expressed are those of the authors and not those of the U.S. Census Bureau.
Citation
Ryan Janicki. Andrew M. Raim. Scott H. Holan. Jerry J. Maples. "Bayesian nonparametric multivariate spatial mixture mixed effects models with application to American Community Survey special tabulations." Ann. Appl. Stat. 16 (1) 144 - 168, March 2022. https://doi.org/10.1214/21-AOAS1494
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