A model-based framework for air quality indices and population risk evaluation, with an application to the analysis of Scottish air quality data
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DOI: rssc.12001
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Cited by:
- Alessandro Fassò & Francesco Finazzi & Ferdinand Ndongo, 2016. "European Population Exposure to Airborne Pollutants Based on a Multivariate Spatio-Temporal Model," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 21(3), pages 492-511, September.
- Guowen Huang & Patrick E. Brown & Sze Hang Fu & Hwashin Hyun Shin, 2022. "Daily mortality/morbidity and air quality: Using multivariate time series with seasonally varying covariances," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(1), pages 148-174, January.
- Guido Fioravanti & Michela Cameletti & Sara Martino & Giorgio Cattani & Enrico Pisoni, 2022. "A spatiotemporal analysis of NO2 concentrations during the Italian 2020 COVID‐19 lockdown," Environmetrics, John Wiley & Sons, Ltd., vol. 33(4), June.
- Francesco Finazzi, 2020. "Fulfilling the information need after an earthquake: statistical modelling of citizen science seismic reports for predicting earthquake parameters in near realtime," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 857-882, June.
- Finazzi, Francesco & Fassò, Alessandro, 2014. "D-STEM: A Software for the Analysis and Mapping of Environmental Space-Time Variables," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 62(i06).
- Andreas Piter & Philipp Otto & Hamza Alkhatib, 2022. "The Helsinki bike‐sharing system—Insights gained from a spatiotemporal functional model," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(3), pages 1294-1318, July.
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