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20192025
most citedA joint bayesian space-time model to integrate spatially misaligned air pollution data in R-INLA

27 citations · 32 across the 6 of their papers we have counts for

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5 papers · 1 filter

stat.AP2025

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.AP20232 cited

Spatiotemporal modelling of PM concentrations in Lombardy (Italy) -- A comparative study

Philipp Otto, Alessandro Fusta Moro, Jacopo Rodeschini +7

This study presents a comparative analysis of three predictive models with an increasing degree of flexibility: hidden dynamic geostatistical models (HDGM), generalised additive mi…

stat.AP2021

A spatio-temporal analysis of NO concentrations during the Italian 2020 COVID-19 lockdown

Guido Fioravanti, Michela Cameletti, Sara Martino +2

When a new environmental policy or a specific intervention is taken in order to improve air quality, it is paramount to assess and quantify - in space and time - the effectiveness…

stat.AP2020

Spatio-temporal modelling of daily concentrations in Italy using the SPDE approach

Guido Fioravanti, Sara Martino, Michela Cameletti +1

This paper illustrates the main results of a spatio-temporal interpolation process of concentrations at daily resolution using a set of 410 monitoring sites, distr…

stat.AP202027 cited

A joint bayesian space-time model to integrate spatially misaligned air pollution data in R-INLA

Chiara Forlani, Samir Bhatt, Michela Cameletti +2

In air pollution studies, dispersion models provide estimates of concentration at grid level covering the entire spatial domain, and are then calibrated against measurements from m…