2019-02-19T10:40:57Zhttp://repositori.uji.es/oai/requestoai:repositori.uji.es:10234/1669272018-10-31T11:58:04Zcom_10234_7037com_10234_9col_10234_8635
00925njm 22002777a 4500
dc
Reyes, Adriana
author
Giraldo, Ramón
author
Mateu, Jorge
author
2015
Recently several methodologies for carrying out geostatistical analysis of functional
data has been proposed. All of them assume that the spatial functional process
considered is stationary. However in practice we often have nonstationary functional
data sets because there is spatial trend in the mean. Here we propose a methodology
to extend kriging predictors for functional data to the case where the mean function
is not constant through the region of interest. We consider an approach based on
the classical residual kriging method used in univariate geostatistcs. We propose a
three steps procedure. Initially a functional regression model is used for detrending
the mean. Posteriorly we apply kriging methods for functional data to the regression
residuals for doing prediction of a residual curve on a non-data location. Finally the
prediction curve is obtained as the sum of the trend and the residual prediction.
We apply the methodology to a salinity data set corresponding to 21 salinity curves
recorded a the Ci´enaga Grande de Santa Marta estuary, located in the Caribbean
coast of Colombia. A cross-validation analysis was carried out in order to establish
the performance of the methodology proposed.
REYES, Adriana; GIRALDO, Ramón; MATEU, Jorge. Residual kriging for functional data. Application to the spatial prediction of salinity curves. Communications in Statistics Theory and Methods (2015), v. 44, n. 4, pp. 798-809
http://hdl.handle.net/10234/166927
http://dx.doi.org/10.1080/03610926.2012.753087
Cross-validation
Functional linear model
Residual kriging
Salinity
Residual kriging for functional data. Application to the spatial prediction of salinity curves