Bayesian surface regression versus spatial spectral nonparametric curve regression
Identificadores
URI: http://hdl.handle.net/10481/76572Metadatos
Afficher la notice complèteEditorial
Elsevier
Materia
Bayesian estimation Nonparametric estimation Spatial curve regression Spatial periodogram operator Spatial spectral density operator Surface regression
Date
2021-10-30Referencia bibliográfica
Published version: M.D. Ruiz–Medina, D. Miranda, Bayesian surface regression versus spatial spectral nonparametric curve regression, Spatial Statistics, Volume 50, 2022, 100604, ISSN 2211-6753, [https://doi.org/10.1016/j.spasta.2022.100604]
Patrocinador
MCIN/AEI/PGC2018-099549-B-I00; CEX2020-001105-M MCIN/AEI/10.13039/501100011033Résumé
COVID{19 incidence is analyzed at the provinces of some Spanish
Communities during the period February{October, 2020. Two in nite{
dimensional regression approaches are tested. The rst one is implemented
in the regression framework introduced in Ruiz{Medina, Miranda and Espejo
[70]. Speci cally, a bayesian framework is adopted in the estimation
of the pure point spectrum of the temporal autocorrelation operator, characterizing
the second{order structure of a surface sequence. The second
approach is formulated in the context of spatial curve regression. A nonparametric
estimator of the spectral density operator, based on the spatial
periodogram operator, is computed to approximate the spatial correlation
between curves. Dimension reduction is achieved by projection onto the
empirical eigenvectors of the long{run spatial covariance operator. Cross{
validation procedures are implemented to test the performance of the two
functional regression approaches.