Sampled Grid Pairwise Likelihood (SG-PL): An Efficient Approach for Spatial Regression on Large Data

Abstract

Estimating spatial regression models on large, irregularly structured datasets poses significant computational hurdles. While Pairwise Likelihood (PL) methods offer a pathway to simplify these estimations, the efficient selection of informative observation pairs remains a critical challenge, particularly as data volume and complexity grow. This paper introduces the Sampled Grid Pairwise Likelihood (SG-PL) method, a novel approach that employs a grid-based sampling strategy to strategically select observation pairs. Simulation studies demonstrate SG-PL's principal advantage: a dramatic reduction in computational time -- often by orders of magnitude -- when compared to benchmark methods. This substantial acceleration is achieved with a manageable trade-off in statistical efficiency. An empirical application further validates SG-PL's practical utility. Consequently, SG-PL emerges as a highly scalable and effective tool for spatial analysis on very large datasets, offering a compelling balance where substantial gains in computational feasibility are realized for a limited cost in statistical precision, a trade-off that increasingly favors SG-PL with larger N.