Journal of the Korean Statistical Society

The Korean Statistical Society (한국통계학회)
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A FRAMEWORK TO UNDERSTAND THE ASYMPTOTIC PROPERTIES OF KRIGING AND SPLINES

  • Furrer Eva M. (Institute for Mathematics Applied to Geosciences, National Center for Atmospheric Research) ;
  • Nychka Douglas W. (Institute for Mathematics Applied to Geosciences, National Center for Atmospheric Research)
  • Published : 2007.03.31
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Abstract

Kriging is a nonparametric regression method used in geostatistics for estimating curves and surfaces for spatial data. It may come as a surprise that the Kriging estimator, normally derived as the best linear unbiased estimator, is also the solution of a particular variational problem. Thus, Kriging estimators can also be interpreted as generalized smoothing splines where the roughness penalty is determined by the covariance function of a spatial process. We build off the early work by Silverman (1982, 1984) and the analysis by Cox (1983, 1984), Messer (1991), Messer and Goldstein (1993) and others and develop an equivalent kernel interpretation of geostatistical estimators. Given this connection we show how a given covariance function influences the bias and variance of the Kriging estimate as well as the mean squared prediction error. Some specific asymptotic results are given in one dimension for Matern covariances that have as their limit cubic smoothing splines.

Keywords

References

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