A comparison of a traditional geostatistical regression approach and a general Gaussian process approach for spatial prediction

DOI10.1002/sta4.57MaRDI QIDQ6537795

Daniel Cooley, Stanley Leung

Publication date: 14 May 2024

Published in: Stat (Search for Journal in Brave)

zbMATH Keywords

krigingmachine learningsoil moisturenon-stationary spatial models

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

• Unnamed Item
• A class of covariate-dependent spatiotemporal covariance functions for the analysis of daily ozone concentration
• Model-based geostatistics.
• Statistical Methods for Spatial Data Analysis
• Multiresolution models for nonstationary spatial covariance functions
• Bayesian Spatial Modeling of Extreme Precipitation Return Levels
• Spectral methods for nonstationary spatial processes
• Strictly Proper Scoring Rules, Prediction, and Estimation
• Inconsistent Estimation and Asymptotically Equal Interpolations in Model-Based Geostatistics

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