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.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…
stat.CO2025
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…