4 papers
Kriging Beyond Gaussianity: Fixed-Domain Asymptotic Optimality
Moreno Bevilacqua, Hector Araya
Under squared-error loss, the optimal predictor of a non-Gaussian random field is its conditional mean, which may be difficult to compute. We consider a broad class of non-Gaussian…
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…
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…
Parsimonious Compactly Supported Covariance Models in the Gauss Hypergeometric Class: Identifiability, Reparameterizations, and Asymptotic Properties
Moreno Bevilacqua, Christian Caamaño-Carrillo, Tarik Faouzi +1
We study covariance functions in the Gauss hypergeometric () class, a flexible family that encompasses the Generalized Wendland () and Matérn ($\mathcal…