24 citations · 88 across the 17 of their papers we have counts for
19 papers
Unified Native Spaces in Kernel Methods
Xavier Emery, Emilio Porcu, Moreno Bevilacqua
There exists a plethora of parametric models for positive definite kernels, and their use is ubiquitous in disciplines as diverse as statistics, machine learning, numerical analysi…
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…
The Matérn Model: A Journey through Statistics, Numerical Analysis and Machine Learning
Emilio Porcu, Moreno Bevilacqua, Robert Schaback +1
The Matérn model has been a cornerstone of spatial statistics for more than half a century. More recently, the Matérn model has been central to disciplines as diverse as numerical…
Convergence Arguments to Bridge Cauchy and Matérn Covariance Functions
Tarik Faouzi, Emilio Procu, Igor Kondrashuk +1
The Matérn and the Generalized Cauchy families of covariance functions have a prominent role in spatial statistics as well as in a wealth of statistical applications. The Matérn fa…
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…
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…
Asymptotically Equivalent Prediction in Multivariate Geostatistics
François Bachoc, Emilio Porcu, Moreno Bevilacqua +2
Cokriging is the common method of spatial interpolation (best linear unbiased prediction) in multivariate geostatistics. While best linear prediction has been well understood in un…
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…
Zastavnyi Operators and Positive Definite Radial Functions
T. Faouzi, E. Porcu, M. Bevilacqua +1
Positive definite functions are fundamental to many areas of applied mathematics, probability theory, spatial statistics and machine learning, amogst others. Motivated by a problem…
Composite likelihood estimation for a gaussian process under fixed domain asymptotics
François Bachoc, Moreno Bevilacqua, Daira Velandia
We study the problem of estimating the covariance parameters of a one-dimensional Gaussian process with exponential covariance function under fixed-domain asymptotics. We show that…
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…
Estimating covariance functions of multivariate skew-Gaussian random fields on the sphere
Alfredo Alegría, Sandra Caro, Moreno Bevilacqua +2
This paper considers a multivariate spatial random field, with each component having univariate marginal distributions of the skew-Gaussian type. We assume that the field is define…
Estimation and Prediction using generalized Wendland Covariance Functions under fixed domain asymptotics
M. Bevilacqua, T. Faouzi, R. Furrer +1
We study estimation and prediction of Gaussian random fields with covariance models belonging to the generalized Wendland (GW) class, under fixed domain asymptotics. As the Matérn…
Buhmann covariance functions, their compact supports, and their smoothness
E. Porcu, P. Zastavyi, M. Bevilacqua
We consider the Buhmann class of compactly supported radial basis functions, whih includes a wealth of special cases that have been studied in both numerical analysis and spatial s…
Maximum likelihood estimation for a bivariate Gaussian process under fixed domain asymptotics
Daira Velandia, François Bachoc, Moreno Bevilacqua +2
We consider maximum likelihood estimation with data from a bivariate Gaussian process with a separable exponential covariance model under fixed domain asymptotic. We first characte…
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].
Validity of covariance models for the analysis of geographical variation
Gilles Guillot, René Schilling, Emilio Porcu +1
Due to the availability of large molecular data-sets, covariance models are increasingly used to describe the structure of genetic variation as an alternative to more heavily param…
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…