RT Journal Article
T1 A spatial functional count model for heterogeneity analysis in time
A1 Torres-Signes, Antoni
A1 Frías, María P.
A1 Mateu, Jorge
A1 Ruiz-Medina, María D.
K1 Modelos matemáticos
AB 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.
YR 2021
FD 2021
LK https://hdl.handle.net/10630/30285
UL https://hdl.handle.net/10630/30285
LA eng
NO 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
NO This work was supported by MCIU/AEI/ERDF, UE grant PGC2018-099549-B-I00 (M.D. Ruiz–Medina, M.P. Frías, A. Torres-Signes), PID2019-107392RB-100 (J. Mateu), and by Grant A-FQM-345-UGR18 (M.D. Ruiz–Medina, M.P. Frías, A. Torres-Signes) cofinanced by ERDF Operational Programme 2014–2020 and the Economy and Knowledge Council of the Regional Government of Andalusia, Spain.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 21 ene 2026