@misc{10481/72906,
year = {2016},
month = {2},
url = {http://hdl.handle.net/10481/72906},
abstract = {This paper is focus on spatial functional variables whose observations are a set of spatially correlated
sample curves obtained as realizations of a spatio-temporal
stochastic process. In this context, as alternative to other
geostatistical techniques (kriging, kernel smoothing,
among others), a new method to predict the curves of
temporal evolution of the process at unsampled locations
and also the surfaces of geographical evolution of the
variable at unobserved time points is proposed. In order to
test the good performance of the proposed method, two
simulation studies and an application with real climatological data have been carried out. Finally, the results were
compared with ordinary functional kriging.},
organization = {Project P11-FQM-8068 from Consejería de Innovación, Ciencia y Empresa, Junta de Andalucía, Spain},
organization = {Projects MTM2013-47929-P, MTM2011-28285-C02-C2 and MTM 2014-52184-P from Secretaría de
Estado Investigación, Desarrollo e Innovación, Ministerio de Economía y Competitividad, Spain},
publisher = {Springer},
keywords = {Spatial functional data},
keywords = {Spatial correlation},
keywords = {P-spline penalty},
keywords = {Functional regression},
title = {Prediction of functional data with spatial dependence: a penalized approach},
doi = {https://doi.org/10.1007/s00477-016-1216-8},
author = {Aguilera Morillo, María del Carmen and Durbán, María and Aguilera Del Pino, Ana María},
}