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A comparison of kriging with nonparametric regression methods

Author

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  • Yakowitz, S. J.
  • Szidarovszky, F.

Abstract

"Kriging" is the name of a parametric regression method used by hydrologists and mining engineers, among others. Features of the kriging approach are that it also provides an error estimate and that it can conveniently be employed also to estimate the integral of the regression function. In the present work, the kriging method is described and some of its statistical characteristics are explored. Also, some extensions of the nonparametric regression approach are made so that it too displays the kriging features. In particular, a "data driven" estimator of the expected square error is derived. Theoretical and computational comparisons of the kriging and nonparametric regressors are offered.

Suggested Citation

  • Yakowitz, S. J. & Szidarovszky, F., 1985. "A comparison of kriging with nonparametric regression methods," Journal of Multivariate Analysis, Elsevier, vol. 16(1), pages 21-53, February.
    • Handle: RePEc:eee:jmvana:v:16:y:1985:i:1:p:21-53
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    Cited by:

    1. François Bachoc & Emile Contal & Hassan Maatouk & Didier Rullière, 2017. "Gaussian processes for computer experiments," Post-Print hal-01665936, HAL.
    2. Toshihiro Hirano & Yoshihiro Yajima, 2013. "Covariance tapering for prediction of large spatial data sets in transformed random fields," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 65(5), pages 913-939, October.
    3. Almohammadi, K.M. & Harby, K., 2020. "Operational conditions optimization of a proposed solar-powered adsorption cooling system: Experimental, modeling, and optimization algorithm techniques," Energy, Elsevier, vol. 206(C).
    4. Wenpin Tang & Lu Zhang & Sudipto Banerjee, 2021. "On identifiability and consistency of the nugget in Gaussian spatial process models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(5), pages 1044-1070, November.

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