Prediction of functional data with spatial dependence: a penalized approach Articles uri icon

publication date

  • January 2017

start page

  • 7

end page

  • 22

issue

  • 1

volume

  • 31

international standard serial number (ISSN)

  • 1436-3240

electronic international standard serial number (EISSN)

  • 1436-3259

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.

keywords

  • spatial functional data; spatial correlation; p-spline penalty; functional regression; r package; regression; splines; models