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A frequentist approach to computer model calibration

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  • Raymond K. W. Wong
  • Curtis B. Storlie
  • Thomas C. M. Lee

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  • Raymond K. W. Wong & Curtis B. Storlie & Thomas C. M. Lee, 2017. "A frequentist approach to computer model calibration," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(2), pages 635-648, March.
    • Handle: RePEc:bla:jorssb:v:79:y:2017:i:2:p:635-648
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    References listed on IDEAS

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    1. S. Conti & J. P. Gosling & J. E. Oakley & A. O'Hagan, 2009. "Gaussian process emulation of dynamic computer codes," Biometrika, Biometrika Trust, vol. 96(3), pages 663-676.
    2. Storlie, Curtis B. & Reich, Brian J. & Helton, Jon C. & Swiler, Laura P. & Sallaberry, Cedric J., 2013. "Analysis of computationally demanding models with continuous and categorical inputs," Reliability Engineering and System Safety, Elsevier, vol. 113(C), pages 30-41.
    3. Marc C. Kennedy & Anthony O'Hagan, 2001. "Bayesian calibration of computer models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(3), pages 425-464.
    4. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521780506.
    5. D. Huang & T. Allen & W. Notz & N. Zeng, 2006. "Global Optimization of Stochastic Black-Box Systems via Sequential Kriging Meta-Models," Journal of Global Optimization, Springer, vol. 34(3), pages 441-466, March.
    6. Storlie, Curtis B. & Swiler, Laura P. & Helton, Jon C. & Sallaberry, Cedric J., 2009. "Implementation and evaluation of nonparametric regression procedures for sensitivity analysis of computationally demanding models," Reliability Engineering and System Safety, Elsevier, vol. 94(11), pages 1735-1763.
    7. Curtis B. Storlie & William A. Lane & Emily M. Ryan & James R. Gattiker & David M. Higdon, 2015. "Calibration of Computational Models With Categorical Parameters and Correlated Outputs via Bayesian Smoothing Spline ANOVA," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 68-82, March.
    8. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521785167.
    9. Drignei, Dorin & Morris, Max D., 2006. "Empirical Bayesian Analysis for Computer Experiments Involving Finite-Difference Codes," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1527-1536, December.
    10. Jeremy E. Oakley & Anthony O'Hagan, 2004. "Probabilistic sensitivity analysis of complex models: a Bayesian approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(3), pages 751-769, August.
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    Cited by:

    1. Jung, Yongsu & Jo, Hwisang & Choo, Jeonghwan & Lee, Ikjin, 2022. "Statistical model calibration and design optimization under aleatory and epistemic uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    2. Huaimin Diao & Yan Wang & Dianpeng Wang, 2022. "A D-Optimal Sequential Calibration Design for Computer Models," Mathematics, MDPI, vol. 10(9), pages 1-15, April.
    3. Yang Sun & Xiangzhong Fang, 2024. "A new $$L_2$$ L 2 calibration procedure of computer models based on the smoothing spline ANOVA," Statistical Papers, Springer, vol. 65(4), pages 1901-1926, June.
    4. Sun, Yang & Fang, Xiangzhong, 2024. "Efficient calibration of computer models with multivariate output," Journal of Multivariate Analysis, Elsevier, vol. 202(C).
    5. Michael Kohler & Adam Krzyżak, 2020. "Estimating quantiles in imperfect simulation models using conditional density estimation," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 72(1), pages 123-155, February.
    6. Benedict Götz & Sebastian Kersting & Michael Kohler, 2021. "Estimation of an improved surrogate model in uncertainty quantification by neural networks," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 73(2), pages 249-281, April.
    7. SungKu Kang & Ran Jin & Xinwei Deng & Ron S. Kenett, 2023. "Challenges of modeling and analysis in cybermanufacturing: a review from a machine learning and computation perspective," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 415-428, February.
    8. Yang Sun & Xiangzhong Fang, 2025. "Calibration for Computer Models with Time-Varying Parameter," Mathematics, MDPI, vol. 13(18), pages 1-22, September.
    9. Wang, Bo & Zhang, Qiong & Xie, Wei, 2019. "Bayesian sequential data collection for stochastic simulation calibration," European Journal of Operational Research, Elsevier, vol. 277(1), pages 300-316.

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