3 papers
stat.CO2026
A new framework for non-stationary spatio-temporal data fusion of multi-fidelity models
Pietro Colombo, Fabio Sigrist, Claire Miller +3
We propose a new scalable framework for spatio-temporal data fusion with multi-fidelity Gaussian processes (MFGPs) that enables fully likelihood-based inference for both stationary…
stat.AP2026
On the use of satellite information to estimate agricultural carbon footprint in a small area framework
Riccardo Pajno, Felicetta Carillo, Paolo Maranzano +2
The agricultural sector is undergoing rapid change due to climate pressures, demographic shifts, and uneven economic development, increasing the demand for reliable environmental i…
stat.AP2025
Spatiotemporal clustering of GHGs emissions in Europe: exploring the role of spatial component
Caterina Morelli, Paolo Maranzano, Philipp Otto
In this study, we propose a novel application of spatiotemporal clustering in the environmental sciences, with a particular focus on regionalised time series of greenhouse gases (G…