Prediction of functional data with spatial dependence: a penalized approach

  • M. Carmen Aguilera-Morillo
  • María Durbán
  • Ana M. Aguilera
Original Paper

DOI: 10.1007/s00477-016-1216-8

Cite this article as:
Aguilera-Morillo, M.C., Durbán, M. & Aguilera, A.M. Stoch Environ Res Risk Assess (2017) 31: 7. doi:10.1007/s00477-016-1216-8


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.


Spatial functional data Spatial correlation P-spline penalty Functional regression 

Funding information

Funder NameGrant NumberFunding Note
Consejería de Innovación, Ciencia y Empresa. Junta de Andalucía, Spain
  • P11-FQM-8068
Secretaría de Estado Investigación, Desarrollo e Innovación, Ministerio de Economía y Competitividad, Spain
  • MTM2013-47929-P
  • MTM 2011-28285-C02-C2
Secretaría de Estado Investigación, Desarrollo e Innovación, Ministerio de Econom?a y Competitividad, Spain
  • MTM 2014-52184-P

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • M. Carmen Aguilera-Morillo
    • 1
  • María Durbán
    • 1
  • Ana M. Aguilera
    • 2
  1. 1.University Carlos III de MadridLeganésSpain
  2. 2.University of GranadaGranadaSpain

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