Residual Kriging for Functional Spatial Prediction of Salinity Curves

• Published in: Communications in statistics. Theory and methods Vol. 44; no. 4; pp. 798 - 809
• Main Authors: Reyes, Adriana, Giraldo, Ramón, Mateu, Jorge
• Format: Journal Article
• Language: English
• Published: Philadelphia Taylor & Francis 16.02.2015; Taylor & Francis Ltd
• Subjects: Coastal environments; Constants; Cross-validation; Functional linear model; Geostatistics; Kriging; Mathematical models; Regression; Residual kriging; Salinity; Statistics; Trends
• ISSN: 0361-0926; 1532-415X
• DOI: 10.1080/03610926.2012.753087

Recently, several methodologies to perform geostatistical analysis of functional data have been proposed. All of them assume that the spatial functional process considered is stationary. However, in practice, we often have nonstationary functional data because there exists an explicit 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 geostatistics. We propose a three steps procedure. Initially, a functional regression model is used to detrend the mean. Then we apply kriging methods for functional data to the regression residuals to predict a residual curve at 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 salinity data corresponding to 21 salinity curves recorded at the Ciénaga Grande de Santa Marta estuary, located in the Caribbean coast of Colombia. A cross-validation analysis was carried out to track the performance of the proposed methodology.