A framework maps erosion distributions to Wasserstein space, uses basis expansion to create a multivariate random field, and applies local regression plus Kriging to predict distributions and their functionals at new locations, outperforming alternatives in simulations and applied to Shaanxi provinc
and Reich, Brian J
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
A random scale mixture process with amortized Bayesian inference enables scalable modeling of spatially dependent extreme temperatures and associated heat risks.
citing papers explorer
-
Spatial Prediction of Local Soil Erosion Distribution in the Wasserstein Space
A framework maps erosion distributions to Wasserstein space, uses basis expansion to create a multivariate random field, and applies local regression plus Kriging to predict distributions and their functionals at new locations, outperforming alternatives in simulations and applied to Shaanxi provinc
-
Spatial Extremes at Scale: A Case Study of Surface Skin Temperature and Heat Risk in the United States
A random scale mixture process with amortized Bayesian inference enables scalable modeling of spatially dependent extreme temperatures and associated heat risks.