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Activity Number: 454 - Advances in Spatial and Spatio-Temporal Methodology with Applications to Official Statistics
Type: Topic Contributed
Date/Time: Wednesday, August 2, 2017 : 8:30 AM to 10:20 AM
Sponsor: Government Statistics Section
Abstract #322738
Title: Multivariate Spatio-Temporal Survey Fusion with Application to the American Community Survey and Local Area Unemployment Statistics
Author(s): Scott H. Holan* and Jonathan R Bradley and Christopher Wikle
Companies: University of Missouri and Florida State University and University of Missouri
Keywords: American Community Survey ; Bayesian ; Data Fusion ; Hierarchical ; Multivariate ; Spatio-Temporal

There are often multiple surveys available that estimate and report related demographic variables of interest that are referenced over space and/or time. Not all surveys produce the same information, and thus, combining these surveys typically leads to higher quality estimates. By combining surveys using a Bayesian approach, we can account for different margins of error and leverage dependencies to produce estimates of every variable considered at every spatial location and every time point. Specifically, our strategy is to use a hierarchical modeling approach, where the first stage of the model incorporates the margin of error associated with each survey. Then, in a lower stage of the hierarchical model, the multivariate spatio-temporal mixed effects model is used to incorporate multivariate spatio-temporal dependencies of the processes of interest. To demonstrate our proposed methodology, we jointly analyze period estimates from the US Census Bureau's American Community Survey, and estimates obtained from the Bureau of Labor Statistics Local Area Unemployment Statistics program.

Authors who are presenting talks have a * after their name.

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