JSM2024
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Invited Paper Session

Bayesian Dependent Data Models and Machine Learning for Official Statistics and Survey Methodology

Sun, Aug 4, 2:00 PM - 3:50 PM
Scott HolanOrganizerYajuan SiChair
Survey Research Methods Section

4 Presentations

Bayesian Hierarchical Models For Multi-type Survey Data Using Spatially Correlated Covariates Measured With Error
2:05 PM - 2:30 PM
Scott Holan (University of Missouri/U.S. Census Bureau)
Co-authors: Saikat Nandy (St. Jude Children's Research Hospital), Jonathan Bradley (Florida State University), Christopher Wikle (University of Missouri-Columbia)
Incorporating heterogeneous types of uncertainty in small area estimates from multiple demographic data sources
2:30 PM - 2:55 PM
Lance Waller (Emory University)
Multi-Source Hierarchical Models for Geographically Granular Retail Sales Estimates
2:55 PM - 3:20 PM
Darcy Morris (U.S. Census Bureau)
Statistical Deep Learning for Dependent Establishment Data
3:20 PM - 3:45 PM
Paul Parker (University of California Santa Cruz)
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