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arXiv · 1407.7479

Mixed Effects Modeling for Areal Data that Exhibit Multivariate-Spatio-Temporal Dependencies

Jonathan R. Bradley·Scott H. Holan·Christopher K. Wikle
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Abstract

There are many data sources available that 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. As such, we introduce the multivariate-spatio-temporal mixed effects model (MSTM) to analyze areal data with multivariate-spatio-temporal dependencies. 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. Several examples are used to demonstrate that the MSTM provides an extremely viable solution to many important problems found in different and distinct corners of the spatio-temporal statistics literature including: modeling nonseparable and nonstationary covariances, combing data from multiple repeated surveys, and analyzing massive multivariate-spatio-temporal datasets.

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Jonathan R. Bradley, Scott H. Holan, Christopher K. Wikle. 2014-09-04. Mixed Effects Modeling for Areal Data that Exhibit Multivariate-Spatio-Temporal Dependencies. https://arxiv.org/abs/1407.7479

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