3 papers
stat.ME2026
Fast Stochastic Nearest Neighbor Pairwise Composite Likelihood for Massive Spatial Datasets
Moreno Bevilacqua, Francisco Cuevas-Pacheco, Christian Caamaño-Carrillo
Weighted pairwise composite likelihoods based on nearest-neighbor (NN) pairs provide a scalable alternative to full likelihood inference for spatial random fields, but can remain e…
stat.CO2026
Fast simulation of Gaussian random fields with flexible correlation models in Euclidean spaces
Moreno Bevilacqua, Xavier Emery, Francisco Cuevas-Pacheco
The efficient simulation of Gaussian random fields with flexible correlation structures is fundamental in spatial statistics, machine learning, and uncertainty quantification. In t…
stat.ME2026
Kriging for large datasets via penalized neighbor selection
Francisco Cuevas-Pacheco, Jonathan Acosta
Kriging is a fundamental tool for spatial prediction, but its computational complexity of becomes prohibitive for large datasets. While local kriging using -nearest nei…