24 citations · 88 across the 17 of their papers we have counts for
6 papers · 1 filter
Matern and Generalized Wendland correlation models that parameterize hole effect, smoothness, and support
Xavier Emery, Moreno Bevilacqua, Emilio Porcu
A huge literature in statistics and machine learning is devoted to parametric families of correlation functions, where the correlation parameters are used to understand the propert…
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…
Estimation and prediction of Gaussian processes using generalized Cauchy covariance model under fixed domain asymptotics
Moreno Bevilacqua, Tarik Faouzi
We study estimation and prediction of Gaussian processes with covariance model belonging to the generalized Cauchy (GC) family, under fixed domain asymptotics. Gaussian processes w…
On the Flexibility of Multivariate Covariance Models: Comment on the Paper by Genton and Kleiber
Moreno Bevilacqua, Amanda S. Hering, Emilio Porcu
Discussion of "Cross-Covariance Functions for Multivariate Geostatistics" by Genton and Kleiber [arXiv:1507.08017].
Comparing composite likelihood methods based on pairs for spatial Gaussian random fieldsM
Moreno Bevilacqua, Carlo Gaetan
In the last years there has been a growing interest in proposing methods for estimating covariance functions for geostatistical data. Among these, maximum likelihood estimators hav…