The linear predictor of a spatio-temporal disease mapping model can be expressed as a sum of main and interaction terms, each of these specified by smooth functions of time, space and time and space respectively. We present the use of Pe- nalized Complexity Priors (PC priors) for spatio-temporal smoothing models, where the interaction model shrinks to the model with only main effects.
Revisiting space-time disease mapping models / Franco-Villoria Maria; Ventrucci Massimo; Rue Haavard. - ELETTRONICO. - (2021), pp. 73-76. (Intervento presentato al convegno GRASPA 2021 tenutosi a Virtual Conference organized by Sapienza University of Rome nel 7-12 June 2021).
Revisiting space-time disease mapping models
Ventrucci MassimoSecondo
;
2021
Abstract
The linear predictor of a spatio-temporal disease mapping model can be expressed as a sum of main and interaction terms, each of these specified by smooth functions of time, space and time and space respectively. We present the use of Pe- nalized Complexity Priors (PC priors) for spatio-temporal smoothing models, where the interaction model shrinks to the model with only main effects.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.