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A general statistical model for computer experiments with time series output

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  • Drignei, Dorin

Abstract

Manufacturing processes increasingly rely on computer experimentation as a substitute for costly physical experimentation. However, computer experimentation may not be very efficient because it often relies on computationally intensive simulation (or computer) models. To address this computational problem, this paper proposes a general statistical model as a computationally fast approximation for computer models with time series output. More precisely, the statistical models will be regression models with input-dependent design matrix and input-correlated errors. An example from the automotive industry will be used to illustrate the methodology.

Suggested Citation

  • Drignei, Dorin, 2011. "A general statistical model for computer experiments with time series output," Reliability Engineering and System Safety, Elsevier, vol. 96(4), pages 460-467.
    • Handle: RePEc:eee:reensy:v:96:y:2011:i:4:p:460-467
    DOI: 10.1016/j.ress.2010.11.006
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    References listed on IDEAS

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    1. Blatman, Géraud & Sudret, Bruno, 2010. "Efficient computation of global sensitivity indices using sparse polynomial chaos expansions," Reliability Engineering and System Safety, Elsevier, vol. 95(11), pages 1216-1229.
    2. Saltelli, Andrea & Ratto, Marco & Tarantola, Stefano & Campolongo, Francesca, 2006. "Sensitivity analysis practices: Strategies for model-based inference," Reliability Engineering and System Safety, Elsevier, vol. 91(10), pages 1109-1125.
    3. Higdon, Dave & Gattiker, James & Williams, Brian & Rightley, Maria, 2008. "Computer Model Calibration Using High-Dimensional Output," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 570-583, June.
    4. Iooss, Bertrand & Ribatet, Mathieu, 2009. "Global sensitivity analysis of computer models with functional inputs," Reliability Engineering and System Safety, Elsevier, vol. 94(7), pages 1194-1204.
    5. Campbell, Katherine, 2006. "Statistical calibration of computer simulations," Reliability Engineering and System Safety, Elsevier, vol. 91(10), pages 1358-1363.
    6. 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.
    7. Trucano, T.G. & Swiler, L.P. & Igusa, T. & Oberkampf, W.L. & Pilch, M., 2006. "Calibration, validation, and sensitivity analysis: What's what," Reliability Engineering and System Safety, Elsevier, vol. 91(10), pages 1331-1357.
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    Cited by:

    1. Yuan, Jun & Ng, Szu Hui, 2013. "A sequential approach for stochastic computer model calibration and prediction," Reliability Engineering and System Safety, Elsevier, vol. 111(C), pages 273-286.

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