2 citations · 5 across the 7 of their papers we have counts for
5 papers · 1 filter
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…