RT Journal Article
T1 Log-Gaussian Cox Process in Infinite-Dimensional Spaces.
A1 Torres-Signes, Antoni
A1 Frías, María P.
A1 Ruiz-Medina, María D.
K1 Hilbert, Espacio de
K1 Funciones de variable compleja
K1 Poisson, Procesos de
AB This paper introduces new results on doubly stochastic Poisson processes, with log-Gaussian Hilbert-valued random intensity (LGHRI), defined from the Ornstein-Uhlenbeck process (O-U process) in Hilbert spaces. Sufficient conditions are derived for the existence of a counting measure on l2, for this type of doubly stochastic Poisson processes. Functional parameter estimation and prediction is achieved from the discrete-time approximation of the Hilbert-valued O-U process by an autoregressive Hilbertian process of order one (ARH(1) process). The results derived are applied to functional prediction of spatiotemporal log-Gaussian Cox processes, and an application to functional disease mapping is developed. The numerical results given, from the conditional simulation study undertaken, are compared to those ones obtained, when the random intensity is assumed to be a spatiotemporal long-range dependence (LRD) log-Gaussian process.
PB American Mathematical Society
YR 2018
FD 2018
LK https://hdl.handle.net/10630/32365
UL https://hdl.handle.net/10630/32365
LA eng
NO Política de acceso abierto tomada de: https://v2.sherpa.ac.uk/id/publication/7814
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 21 ene 2026