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oai:riuma.uma.es:10630/302822026-02-03T10:56:31Zcom_10630_2254col_10630_37953

00925njm 22002777a 4500 dc Frías, María P. author Torres-Signes, Antoni author Ruiz-Medina, María D. author Mateu, Jorge author 2022 We introduce a new class of spatial Cox processes driven by a Hilbert-valued random log-intensity. We adopt a parametric framework in the spectral domain, to estimate its spatial functional correlation structure. Specifically, we consider a spectral functional, approach based on the periodogram operator, inspired on Whittle estimation methodology. Strong consistency of the parametric estimator is proved in the linear case. We illustrate this property in a simulation study under a Gaussian first-order Spatial Autoregressive Hilbertian scenario for the log-intensity model. Our method is applied to the spatial functional prediction of respiratory disease mortality in the Spanish Iberian Peninsula, in the period 1980–2015. Frías, M.P., Torres-Signes, A., Ruiz-Medina, M.D. et al. Spatial Cox processes in an infinite-dimensional framework. TEST 31, 175–203 (2022). https://doi.org/10.1007/s11749-021-00773-z https://hdl.handle.net/10630/30282 10.1007/s11749-021-00773-z Medicina - Modelos matemáticos Spatial Cox processes in an infinite-dimensional framework