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A Hierarchical Bayesian Spatio-Temporal Model To Estimate the Short-term

Professor Pasquale Valentini, Department of EconomicsUniversity G. d’Annunzio Chieti-Pescara, ITALY

Date:26 August 2019, Monday

Location:S16-05-96, DSAP Computer Lab 4, Faculty of Science

Time:04:00PM to 05:00PM


We introduce a hierarchical spatio-temporal regression model to study the spatial and temporal association existing between health data and air pollution. The model is developed for handling measurements belonging to the exponential family of distributions and allows the spatial and temporal components to be modelled conditionally independently via random variables for the (canonical) transformation of the measurement mean function. A temporal autoregressive convolution with spatially correlated and temporally white innovations is used to model the pollution data. This modelling strategy allows first to predict pollution exposure for each district and then to link them with the health outcomes through a spatial dynamic regression model.

This talk refers to a joint work with Lara Fontanella, Clara Grazian and Luigi Ippoliti