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
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.File in questo prodotto:
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