2025/11/04 by Alan Pearse, Pearse, Alan, David Gunawan +3 · 1 voice · 1 citation
Economics, Econometrics and Finance · Environmental Science · Mathematics · Medicine · #Data-Driven Disease Surveillance #Soil Geostatistics and Mapping #Spatial and Panel Data Analysis #stat.CO #stat.ME
paper · pdf · doi:10.48550/arxiv.2511.02551
In this article, we develop fully Bayesian, copula-based, spatial-statistical models for large, noisy, incomplete, and non-Gaussian spatial data. Our approach includes novel constructions of copulas that accommodate a spatial-random-effects structure, enabling low-rank representations and computationally efficient Bayesian inference. The spatial copula is used in a latent process model of the Bayesian hierarchical spatial-statistical model, and, conditional on the latent copula-based spatial process, the data model handles measurement errors and missing data. Our simulation studies show that a fully Bayesian approach delivers accurate and fast inference for both parameter estimation and spatial-process prediction, outperforming several benchmark methods, including fixed rank kriging (FRK). The new class of copula-based models is used to map atmospheric methane in the Bowen Basin, Queensland, Australia, from Sentinel 5P satellite data.