A0448
Title: Scalable semiparametric spatio-temporal regression for large data analysis
Authors: Ting Fung Ma - University of South Carolina (United States) [presenting]
Fangfang Wang - Worcester Polytechnic Institute (United States)
Jun Zhu - University of Wisconsin - Madison (United States)
Anthony Ives - University of Wisconsin - Madison (United States)
Katarzyna Lewinska - Humboldt-University of Berlin (Germany)
Abstract: With the rapid advances in data acquisition techniques, spatio-temporal data are becoming increasingly abundant in a diverse array of disciplines. Spatio-temporal regression methodology is developed for analyzing large amounts of spatially referenced data collected over time, motivated by environmental studies utilizing remotely sensed satellite data. In particular, a semiparametric autoregressive model without the usual Gaussian assumption and devise a computationally scalable procedure that enables the regression analysis of large datasets is specified. The model parameters by maximum pseudolikelihood are estimated, and it is shown that the computational complexity can be reduced from cubic to linear of the sample size. Asymptotic properties under suitable regularity conditions are further established that inform the computational procedure to be efficient and scalable. A simulation study is conducted to evaluate the finite-sample properties of the parameter estimation and statistical inference. The methodology is illustrated by a dataset with over 2.96 million observations of annual land surface temperature, and a comparison with an existing state-of-the-art approach to spatiotemporal regression highlights the advantages of this method.
