Scalable Semiparametric Spatio-temporal Regression for Large Data Analysis

TL;DR

This paper introduces a scalable semiparametric spatio-temporal regression method for large datasets, particularly useful for environmental remote sensing data, reducing computational complexity and improving analysis efficiency.

Contribution

It develops a novel scalable regression approach that handles large spatio-temporal datasets without Gaussian assumptions, with reduced computational complexity from cubic to linear.

Findings

Method effectively analyzes over 2.96 million observations.

Simulation confirms accurate parameter estimation and inference.

Outperforms existing approaches in real data application.

Abstract

With the rapid advances of data acquisition techniques, spatio-temporal data are becoming increasingly abundant in a diverse array of disciplines. Here we develop spatio-temporal regression methodology for analyzing large amounts of spatially referenced data collected over time, motivated by environmental studies utilizing remotely sensed satellite data. In particular, we specify a semiparametric autoregressive model without the usual Gaussian assumption and devise a computationally scalable procedure that enables the regression analysis of large datasets. We estimate the model parameters by quasi maximum likelihood and show 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. We illustrate our methodology by a dataset with over 2.96 million observations of annual land surface temperature and the comparison with an existing state-of-the-art approach highlights the advantages of our method.