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oai:riuma.uma.es:10630/302852026-02-03T11:28:55Zcom_10630_2254col_10630_37953

Torres-Signes, Antoni Frías, María P. Mateu, Jorge Ruiz-Medina, María D. 2024-02-09T11:37:56Z 2024-02-09T11:37:56Z 2021 Torres-Signes, A., Frías, M.P., Mateu, J. et al. A spatial functional count model for heterogeneity analysis in time. Stoch Environ Res Risk Assess 35, 1825–1849 (2021). https://doi.org/10.1007/s00477-020-01951-5 https://hdl.handle.net/10630/30285 10.1007/s00477-020-01951-5 A spatial curve dynamical model framework is adopted for functional prediction of counts in a spatiotemporal logGaussian 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 l2-valued processes (SARl2 processes) to illustrate the asymptotic properties of the proposed spatial functional estimators. We apply our modelling strategy to spatiotemporal prediction of respiratory disease mortality. eng open access Modelos matemáticos A spatial functional count model for heterogeneity analysis in time journal article