A Cokriging Method for Spatial Functional Data with Applications in Oceanology

  • Pascal Monestiez
  • David Nerini
Part of the Contributions to Statistics book series (CONTRIB.STAT.)

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.


Spatial Covariance Elephant Seal Functional Data Analysis Southern Elephant Seal Antarctic Ocean 
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  1. [1]
    Bailleul F., Charrassin J-B, Ezraty R, Girard-Ardhuin F., McMahon C. R. , Field I. C. and C. Guinet C: Southern elephant seals from Kerguelen Islands confronted by Antarctic Sea ice. Changes in movements and in diving behaviour. Deep Sea Research Part II: Topical Studies in Oceanography 54, 343-355 (2007).CrossRefGoogle Scholar
  2. [2]
    Cardot H., Ferraty F. and P. Sarda: Functional Linear Model. Statistics and Prob-ability Letters 45, 11-22 (1999).MATHCrossRefMathSciNetGoogle Scholar
  3. [3]
    Dabo-Niang S. and A. -F. Yao: Kernel regression estimation for continuous spatial processes. Mathematical Methods for Statistics 16, 298-317 (2007).MATHCrossRefMathSciNetGoogle Scholar
  4. [4]
    Goulard M. and M. Voltz: Linear coregionalization model : tools for estimation and choice of multivariate variograms. Mathematical Geology 24, 269-286 (1992).CrossRefGoogle Scholar
  5. [5]
    Meiring W.: Oscillations and Time Trends in Stratospheric Ozone Levels : A Func-tional Data Analysis Approach. J. Am. Stat. Ass. 102, 788-802 (2007).MATHCrossRefMathSciNetGoogle Scholar
  6. [6]
    Ramsay J. O. and B. W. Silverman: Functional data analysis. Springer, New-York. (2005).Google Scholar
  7. [7]
    Wackernagel H.: Multivariate geostatistics: an introduction with applications. Springer, New-York. (2003).MATHGoogle Scholar

Copyright information

© Physica-Verlag Heidelberg 2008

Authors and Affiliations

  • Pascal Monestiez
    • 1
  • David Nerini
    • 2
  1. 1.Unité de Biostatistique et Processus Spatiaux Domaine Saint PaulFrance
  2. 2.Laboratoire de Microbiologie, Géochimie et Ecologie MarinesCentre d'Océanologie de Marseille CaseFrance

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