5 papers
Scalable model selection for count time series with structural breaks: application to solid-organ transplantation during and after COVID-19 in the USA and Italy
Tobia Filosi, Emiliano Ceccarelli, Emilio Porcu +7
Weekly healthcare activity data are typically non-negative counts with temporal dependence and occasional system-wide disruptions, settings in which Gaussian time-series models may…
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…
Vector-Valued Gaussian Processes and their Kernels on a Class of Metric Graphs
Tobia Filosi, Emilio Porcu, Xavier Emery +2
Despite the increasing importance of stochastic processes on linear networks and graphs, current literature on multivariate (vector-valued) Gaussian random fields on metric graphs…
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…
Computationally Efficient Algorithms for Simulating Isotropic Gaussian Random Fields on Graphs with Euclidean Edges
Alfredo AlegrÃa, Xavier Emery, Tobia Filosi +1
This work addresses the problem of simulating Gaussian random fields that are continuously indexed over a class of metric graphs, termed graphs with Euclidean edges, being more gen…