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Statistical Tests for Cross-Validation of Kriging Models

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  • Kleijnen, Jack

    (Tilburg University, Center For Economic Research)

  • van Beers, W.C.M.

    (Tilburg University, Center For Economic Research)

Abstract

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Suggested Citation

  • Kleijnen, Jack & van Beers, W.C.M., 2019. "Statistical Tests for Cross-Validation of Kriging Models," Discussion Paper 2019-022, Tilburg University, Center for Economic Research.
  • Handle: RePEc:tiu:tiucen:35fba511-2931-47d5-a9ba-30b1229e9093
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    References listed on IDEAS

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    1. D den Hertog & J P C Kleijnen & A Y D Siem, 2006. "The correct Kriging variance estimated by bootstrapping," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(4), pages 400-409, April.
    2. Hui Dong & Marvin K. Nakayama, 2017. "Quantile Estimation with Latin Hypercube Sampling," Operations Research, INFORMS, vol. 65(6), pages 1678-1695, December.
    3. Erickson, Collin B. & Ankenman, Bruce E. & Sanchez, Susan M., 2018. "Comparison of Gaussian process modeling software," European Journal of Operational Research, Elsevier, vol. 266(1), pages 179-192.
    4. Bradley Efron, 2015. "Frequentist accuracy of Bayesian estimates," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 77(3), pages 617-646, June.
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    Cited by:

    1. Aikaterini P. Kyprioti & Alexandros A. Taflanidis & Norberto C. Nadal-Caraballo & Madison O. Campbell, 2021. "Incorporation of sea level rise in storm surge surrogate modeling," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 105(1), pages 531-563, January.
    2. Nuno Costa & Paulo Fontes, 2020. "Energy-Efficiency Assessment and Improvement—Experiments and Analysis Methods," Sustainability, MDPI, vol. 12(18), pages 1-19, September.

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    More about this item

    Keywords

    validation; cross-validation; Kriging; Gaussian process; extrapolation; convex hull; Monte Carlo Technique;
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