2023/08/10 by Arnab Hazra, Pratik Nag, Hazra, Arnab +5 · 1 citation
Economics, Econometrics and Finance · Environmental Science · #Air Quality Monitoring and Forecasting #Computation (stat.CO) #FOS: Computer and information sciences #Soil Geostatistics and Mapping #Spatial and Panel Data Analysis
paper · pdf · doi:10.48550/arxiv.2308.05812
openalex publication_date 2023/08/10 · openalex created_date 2023/08/15 · openalex updated_date 2026/07/28
Increasingly large and complex spatial datasets pose massive inferential challenges due to high computational and storage costs. Our study is motivated by the KAUST Competition on Large Spatial Datasets 2023, which tasked participants with estimating spatial covariance-related parameters and predicting values at testing sites, along with uncertainty estimates. We compared various statistical and deep learning approaches through cross-validation and ultimately selected the Vecchia approximation technique for model fitting. To overcome the constraints in the R package GpGp, which lacked support for fitting zero-mean Gaussian processes and direct uncertainty estimation-two things that are necessary for the competition, we developed additional R functions. Besides, we implemented certain subsampling-based approximations and parametric smoothing for skewed sampling distributions of the estimators. Our team DesiBoys secured victory in two out of four sub-competitions, validating the effectiveness of our proposed strategies. Moreover, we extended our evaluation to a large real spatial satellite-derived dataset on total precipitable water, where we compared the predictive performances of different models using multiple diagnostics.