2 citations · 5 across the 7 of their papers we have counts for
7 papers
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…
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…
A flexible Clayton-like spatial copula with application to bounded support data
Moreno Bevilacqua, Eloy Alvarado, Christian Caamaño-Carrillo
The Gaussian copula is a powerful tool that has been widely used to model spatial and/or temporal correlated data with arbitrary marginal distributions. However, this kind of model…
Modelling Point Referenced Spatial Count Data: A Poisson Process Approach
Diego Morales-Navarrete, Moreno Bevilacqua, Christian Caamaño-Carrillo +1
Random fields are useful mathematical tools for representing natural phenomena with complex dependence structures in space and/or time. In particular, the Gaussian random field is…
Parametric quantile regression models for fitting double bounded response with application to COVID-19 mortality rate data
Diego I. Gallardo, Marcelo Bourguignon, Yolanda M. Gómez +1
In this paper, we develop two fully parametric quantile regression models, based on power Johnson SB distribution Cancho et al. (2020), for modeling unit interval response at diffe…
Unifying Compactly Supported and Matern Covariance Functions in Spatial Statistics
M. Bevilacqua, C. Caamaño-Carrillo, E. Porcu
The Mat{é}rn family of covariance functions has played a central role in spatial statistics for decades, being a flexible parametric class with one parameter determining the smooth…
Non-Gaussian Geostatistical Modeling using (skew) t Processes
M. Bevilacqua, C. Caamaño, R. B. Arellano Valle +1
We propose a new model for regression and dependence analysis when addressing spatial data with possibly heavy tails and an asymmetric marginal distribution. We first propose a sta…