3 papers
stat.ME2026
INLA-RF: A Hybrid Modeling Strategy for Spatio-Temporal Environmental Data
Mario Figueira, Michela Cameletti, Luca Patelli
Environmental processes often exhibit complex, non-linear patterns and discontinuities across space and time, posing significant challenges for traditional geostatistical modeling…
stat.AP2026
Modeling Spatio-Temporal Dynamics of Obesity in Italian Regions Via Bayesian Beta Regression
Luciano Rota, Raffaele Argiento, Michela Cameletti
In this paper we investigate the spatio-temporal dynamics of obesity rates across Italian regions from 2010 to 2022, aiming to identify spatial and temporal trends and assess poten…
stat.ML2024
S-SIRUS: an explainability algorithm for spatial regression Random Forest
Luca Patelli, Natalia Golini, Rosaria Ignaccolo +1
Random Forest (RF) is a widely used machine learning algorithm known for its flexibility, user-friendliness, and high predictive performance across various domains. However, it is…