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Activity Number:
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310
- Modern Approaches to Small Area Estimation with Spatial Modeling and Machine Learning
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, August 5, 2020 : 10:00 AM to 11:50 AM
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Sponsor:
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Survey Research Methods Section
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Abstract #309710
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Title:
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Bayesian Nonparametric Multivariate Spatial Mixture Mixed Effects Models with Application to American Community Survey Special Tabulations
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Author(s):
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Ryan Janicki* and Andrew M. Raim and Scott H. Holan and Jerry Maples
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Companies:
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U.S. Census Bureau and U.S. Census Bureau and University of Missouri and U.S. Census Bureau
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Keywords:
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Small Area Estimation;
Nonparametric Bayes;
Mixture Models;
American Community Survey
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Abstract:
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Leveraging multivariate spatial dependence to improve the precision of estimates using American Community Survey data and other 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 variety of tabulations. Nevertheless, in situations that exhibit heterogeneity among geographies and/or sparsity in the data, the Gaussian assumptions may be problematic and lead to underperformance. To remedy these situations, we propose a multivariate hierarchical Bayesian mixed effects 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 application to special tabulations of American Community Survey data.
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Authors who are presenting talks have a * after their name.
