A cokriging method for spatial functional data with applications in oceanology

• Main Authors: Monestiez, Pascal, P., 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

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