A spatial functional count model for heterogeneity analysis in time

A spatial curve dynamical model framework is adopted for functional prediction of counts in a spatiotemporal log-Gaussian Cox process model. Our spatial functional estimation approach handles both wavelet-based heterogeneity analysis in time, and spectral analysis in space. Specifically, model fitting is achieved by minimising the information divergence or relative entropy between the multiscale model underlying the data, and the corresponding candidates in the spatial spectral domain. A simulation study is carried out within the family of log-Gaussian Spatial Autoregressive l(2)-valued processes (SARl(2) processes) to illustrate the asymptotic properties of the proposed spatial functional estimators. We apply our modelling strategy to spatiotemporal prediction of respiratory disease mortality.

Automated summary

The study uses a spatial curve dynamical model framework for functional prediction of counts in a spatiotemporal log-Gaussian Cox process model, incorporating wavelet-based heterogeneity analysis in time and spectral analysis in space. Model fitting is achieved through minimizing information divergence or relative entropy between data's underlying multiscale model and candidates in the spatial spectral domain. A simulation study within the family of log-Gaussian Spatial Autoregressive l(2)-valued processes demonstrates the asymptotic properties of the proposed spatial functional estimators, which are then applied to spatiotemporal prediction of respiratory disease mortality.