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Kernel interpolation

Author

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  • Mühlenstädt, Thomas
  • Kuhnt, Sonja

Abstract

Surrogate interpolation models for time-consuming computer experiments are being increasingly used in scientific and engineering problems. A new interpolation method, based on Delaunay triangulations and related to inverse distance weighting, is introduced. This method not only provides an interpolator but also uncertainty bands to judge the local fit, in contrast to methods such as radial basis functions. Compared to the classical Kriging approach, it shows a better performance in specific cases of small data sets and data with non-stationary behavior.

Suggested Citation

  • Mühlenstädt, Thomas & Kuhnt, Sonja, 2011. "Kernel interpolation," Computational Statistics & Data Analysis, Elsevier, vol. 55(11), pages 2962-2974, November.
    • Handle: RePEc:eee:csdana:v:55:y:2011:i:11:p:2962-2974
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    References listed on IDEAS

    as
    1. Hartman, Linda & Hossjer, Ola, 2008. "Fast kriging of large data sets with Gaussian Markov random fields," Computational Statistics & Data Analysis, Elsevier, vol. 52(5), pages 2331-2349, January.
    2. Notz W.I., 2003. "Topics in Optimal Design," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 249-250, January.
    3. Host, Gudmund, 1999. "Kriging by local polynomials," Computational Statistics & Data Analysis, Elsevier, vol. 29(3), pages 295-312, January.
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