June 29: Scott Holan / University of Missouri
“Multivariate Spatio-Temporal Models for High-Dimensional Areal Data with Application to Longitudinal Employer-Household Dynamics”
Hosts: Geography Division (Ratcliffe, Anacker, Henrie)
Many data sources report related variables of interest that are also referenced over geographic regions and time; however, there are relatively few general statistical methods that one can readily use that incorporate these multivariate spatio-temporal dependencies. Additionally, many multivariate spatio-temporal areal data sets are extremely high dimensional, which leads to practical issues when formulating statistical models. For example, we analyze Quarterly Workforce Indicators (QWI) published by the U.S. Census Bureau’s Longitudinal Employer-Household Dynamics (LEHD) program. QWIs are available by different variables, regions, and time points, resulting in millions of tabulations. Despite their already expansive coverage, by adopting a fully Bayesian framework, the scope of the QWIs can be extended to provide estimates of missing values along with associated measures of uncertainty. Motivated by the LEHD, and other applications in federal statistics, we introduce the multivariate spatio-temporal mixed effects model (MSTM), which can be used to efficiently model high-dimensional multivariate spatio- temporal areal data sets. The proposed MSTM extends the notion of Moran’s I basis functions to the multivariate spatio-temporal setting. This extension leads to several methodological contributions, including extremely effective dimension reduction, a dynamic linear model for multivariate spatio-temporal areal processes, and the reduction of a high-dimensional parameter space using a novel parameter model.
Recognized scholars in statistics, survey methodology, demography, economics, geography, social and behavioral sciences, computer science, or closely related areas are invited for short-term visits, primarily during the summer. Scholars will present a seminar based on their research and engage in collaborative research with Census Bureau researchers and staff on problems of data collection, processing, analysis, and dissemination.