Cokriging for spatial functional data

JOURNAL OF MULTIVARIATE ANALYSIS

卷:101

Article

Nerini, David¹  Monestiez, Pascal²  Mante, Claude¹ 

[1] Ctr Oceanol Marseille, CNRS, Lab Microbiol Geochim & Ecol Marines, UMR 6117, F-13288 Marseille, France

[2] INRA, Unite Biostat & Proc Spatiaux, F-84914 Avignon, France

关键词: Functional data analysis;    RKHS;    Functional linear model;    Coregionalization;    Cokriging;    Legendre polynomials;   

DOI  :  10.1016/j.jmva.2009.03.005

来源: Elsevier

【 摘 要 】

This work proposes to generalize the method of kriging when data are spatially sampled curves. A spatial functional linear model is constructed including spatial dependencies between curves. Under some regularity conditions of the curves, an ordinary kriging system is established in the infinite dimensional case. From a practical point-of-view, the decomposition of the curves into a functional basis boils down the problem of kriging in infinite dimension to a standard cokriging on basis coefficients. The methodological developments are illustrated with temperature profiles sampled with dives of elephant seals in the Antarctic Ocean. The projection of sampled profiles into a Legendre polynomial basis is performed with a regularization procedure based on spline smoothing which uses the variance of the sampling devices in order to estimate coefficients by quadrature. (C) 2009 Elsevier Inc. All rights reserved.

【 授权许可】

Free