Simulation and real-data studies show spatial basis functions added to random forests yield consistently strong predictive performance for spatially autocorrelated environmental processes.
arXiv preprint arXiv:2410.04312 , year=
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The Spatial Adapter equips frozen predictors with a spatially regularized orthonormal basis for residuals and derives a closed-form low-rank-plus-noise covariance for spatial prediction and kriging.
A hybrid INLA-RF framework integrates Bayesian spatio-temporal modeling with random forests through two iterative algorithms to improve predictions and uncertainty quantification for environmental data.
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Spatial prediction of environmental processes using random forests: How best to account for spatial dependence?
Simulation and real-data studies show spatial basis functions added to random forests yield consistently strong predictive performance for spatially autocorrelated environmental processes.
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Spatial Adapter: Structured Spatial Decomposition and Closed-Form Covariance for Frozen Predictors
The Spatial Adapter equips frozen predictors with a spatially regularized orthonormal basis for residuals and derives a closed-form low-rank-plus-noise covariance for spatial prediction and kriging.
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INLA-RF: A Hybrid Modeling Strategy for Spatio-Temporal Environmental Data
A hybrid INLA-RF framework integrates Bayesian spatio-temporal modeling with random forests through two iterative algorithms to improve predictions and uncertainty quantification for environmental data.