A cokriging method for spatial functional data with applications in oceanology

International audience We propose a method based on a functional linear model which takes into account the spatial dependencies between sampled functions. The problem of estimating a function when spatial samples are available is turned to a standard cokriging problem for suitable choices of the reg...

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Bibliographic Details
Main Authors: Monestiez, Pascal, Nerini, David
Other Authors: Biostatistique et Processus Spatiaux (BioSP), Institut National de la Recherche Agronomique (INRA), Laboratoire de MicrobiologiE de Géochimie et d'Ecologie Marines (LMGEM), Université de la Méditerranée - Aix-Marseille 2-Centre National de la Recherche Scientifique (CNRS), Sophie Dabo-Niang (Editeur), Frédéric Ferraty (Editeur)
Format: Book Part
Language:English
Published: HAL CCSD 2008
Subjects:
OCEANOGRAPHY
LINEAR MODEL
SPATIAL COVARIANCE
MARINE MAMMALS
GEOSTATISTICS
[MATH]Mathematics [math]
[INFO]Computer Science [cs]
Antarctic
Antarc*
Online Access:https://hal.inrae.fr/hal-02817738
https://doi.org/10.1007/978-3-7908-2062-1_36
Description
Summary:International audience We propose a method based on a functional linear model which takes into account the spatial dependencies between sampled functions. The problem of estimating a function when spatial samples are available is turned to a standard cokriging problem for suitable choices of the regression function. This work is illustrated with environmental data in Antarctic where marine mammals operate as samplers. In the framework of second order stationarity, the application points out some di_culties when estimating the structure of spatial covariance between observations

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