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Robust Kriging models in computer experiments

Author

Listed:
  • Taejin Park

    (Department of Industrial and Systems Engineering, KAIST, Daejeon, Republic of Korea)

  • Bongjin Yum

    (Department of Industrial and Systems Engineering, KAIST, Daejeon, Republic of Korea)

  • Ying Hung

    (Department of Statistics and Biostatistics, Rutgers University, Piscataway, USA)

  • Young-Seon Jeong

    (Department of Industrial Engineering, Chonnam National University, Gwangju, Republic of Korea)

  • Myong K Jeong

    (Department of Industrial and Systems Engineering, Rutgers University, Piscataway, USA)

Abstract

In the Gaussian Kriging model, errors are assumed to follow a Gaussian process. This is reasonable in many cases, but such an assumption is not appropriate for the situations when outliers are present. Large prediction errors may occur in those cases and more robust estimation is critical. In this article, we propose a robust estimation of Kriging parameters by utilizing other loss functions rather than classical L2 criterion. To make these estimators more robust to outliers, the L1 in this article. Mathematical programming formulations are developed upon the idea of support vector machine. A machining experiment data are analysed to verify usefulness of the proposed method.

Suggested Citation

  • Taejin Park & Bongjin Yum & Ying Hung & Young-Seon Jeong & Myong K Jeong, 2016. "Robust Kriging models in computer experiments," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 67(4), pages 644-653, April.
    • Handle: RePEc:pal:jorsoc:v:67:y:2016:i:4:p:644-653
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    Citations

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    Cited by:

    1. Wu, Xu & Kozlowski, Tomasz & Meidani, Hadi, 2018. "Kriging-based inverse uncertainty quantification of nuclear fuel performance code BISON fission gas release model using time series measurement data," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 422-436.
    2. Jack P. C. Kleijnen, 2017. "Comment on Park et al.’s “Robust Kriging in computer experiments”," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(6), pages 739-740, June.

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