Modeling Spatial Extremes Using Non‐Gaussian Spatial Autoregressive Models via Convolutional Neural Networks
Data derived from remote sensing or numerical simulations often have a regular gridded structure and are large in volume, making it challenging to find accurate spatial models that can fill in missing grid cells or simulate the process effectively, especially in the presence of spatial heterogeneity and heavy‐tailed ma...