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Calculating first-order sensitivity measures: A benchmark of some recent methodologies

Author

Listed:
  • Gatelli, D.
  • Kucherenko, S.
  • Ratto, M.
  • Tarantola, S.
Abstract
This work compares three different global sensitivity analysis techniques, namely the state-dependent parameter (SDP) modelling, the random balance designs, and the improved formulas of the Sobol’ sensitivity indices. These techniques are not yet commonly known in the literature. Strengths and weaknesses of each technique in terms of efficiency and computational cost are highlighted, thus enabling the user to choose the more suitable method depending on the computational model analysed. Two test functions proposed in the literature are considered. Computational costs and convergence rates for each function are compared and discussed.

Suggested Citation

  • Gatelli, D. & Kucherenko, S. & Ratto, M. & Tarantola, S., 2009. "Calculating first-order sensitivity measures: A benchmark of some recent methodologies," Reliability Engineering and System Safety, Elsevier, vol. 94(7), pages 1212-1219.
    • Handle: RePEc:eee:reensy:v:94:y:2009:i:7:p:1212-1219
    DOI: 10.1016/j.ress.2008.03.028
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    References listed on IDEAS

    as
    1. Tarantola, S. & Gatelli, D. & Mara, T.A., 2006. "Random balance designs for the estimation of first order global sensitivity indices," Reliability Engineering and System Safety, Elsevier, vol. 91(6), pages 717-727.
    2. Storlie, Curtis B. & Helton, Jon C., 2008. "Multiple predictor smoothing methods for sensitivity analysis: Description of techniques," Reliability Engineering and System Safety, Elsevier, vol. 93(1), pages 28-54.
    3. 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.
    4. Storlie, Curtis B. & Helton, Jon C., 2008. "Multiple predictor smoothing methods for sensitivity analysis: Example results," Reliability Engineering and System Safety, Elsevier, vol. 93(1), pages 55-77.
    5. Sobol’, I.M. & Tarantola, S. & Gatelli, D. & Kucherenko, S.S. & Mauntz, W., 2007. "Estimating the approximation error when fixing unessential factors in global sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 92(7), pages 957-960.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Plischke, Elmar, 2010. "An effective algorithm for computing global sensitivity indices (EASI)," Reliability Engineering and System Safety, Elsevier, vol. 95(4), pages 354-360.
    2. Xu, Yanwen & Kohtz, Sara & Boakye, Jessica & Gardoni, Paolo & Wang, Pingfeng, 2023. "Physics-informed machine learning for reliability and systems safety applications: State of the art and challenges," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    3. Liu, Xiaohang & Zheng, Shansuo & Wu, Xinxia & Chen, Dianxin & He, Jinchuan, 2021. "Research on a seismic connectivity reliability model of power systems based on the quasi-Monte Carlo method," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    4. Rahman, Sharif, 2011. "Global sensitivity analysis by polynomial dimensional decomposition," Reliability Engineering and System Safety, Elsevier, vol. 96(7), pages 825-837.

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