Scalable Spatio-Temporal Autoregressive Models for Large Non-Gaussian Multivariate Data

Abstract

Very large spatio-temporal lattice data are becoming increasingly common across a variety of disciplines. However, estimating interdependence across space, time, and outcomes in large lattice data sets remains challenging, as existing approaches are often (i) not scalable, (ii) designed for conditionally Gaussian outcome data, or (iii) are limited to cross-sectional and univariate outcomes. This paper proposes an MCEM estimation strategy for a family of latent-Gaussian multivariate spatio-temporal models that addresses these issues. It also describes and demonstrates an efficient implementation of the proposed algorithm in the R programming language.