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A general first-order global sensitivity analysis method

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  • Xu, Chonggang
  • Gertner, George Zdzislaw

Abstract

Fourier amplitude sensitivity test (FAST) is one of the most popular global sensitivity analysis techniques. The main mechanism of FAST is to assign each parameter with a characteristic frequency through a search function. Then, for a specific parameter, the variance contribution can be singled out of the model output by the characteristic frequency. Although FAST has been widely applied, there are two limitations: (1) the aliasing effect among parameters by using integer characteristic frequencies and (2) the suitability for only models with independent parameters. In this paper, we synthesize the improvement to overcome the aliasing effect limitation [Tarantola S, Gatelli D, Mara TA. Random balance designs for the estimation of first order global sensitivity indices. Reliab Eng Syst Safety 2006; 91(6):717–27] and the improvement to overcome the independence limitation [Xu C, Gertner G. Extending a global sensitivity analysis technique to models with correlated parameters. Comput Stat Data Anal 2007, accepted for publication]. In this way, FAST can be a general first-order global sensitivity analysis method for linear/nonlinear models with as many correlated/uncorrelated parameters as the user specifies. We apply the general FAST to four test cases with correlated parameters. The results show that the sensitivity indices derived by the general FAST are in good agreement with the sensitivity indices derived by the correlation ratio method, which is a non-parametric method for models with correlated parameters.

Suggested Citation

  • Xu, Chonggang & Gertner, George Zdzislaw, 2008. "A general first-order global sensitivity analysis method," Reliability Engineering and System Safety, Elsevier, vol. 93(7), pages 1060-1071.
    • Handle: RePEc:eee:reensy:v:93:y:2008:i:7:p:1060-1071
    DOI: 10.1016/j.ress.2007.04.001
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    References listed on IDEAS

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    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. Saltelli A. & Tarantola S., 2002. "On the Relative Importance of Input Factors in Mathematical Models: Safety Assessment for Nuclear Waste Disposal," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 702-709, September.
    3. Helton, J.C. & Johnson, J.D. & Sallaberry, C.J. & Storlie, C.B., 2006. "Survey of sampling-based methods for uncertainty and sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 91(10), pages 1175-1209.
    4. Kanso, A. & Chebbo, G. & Tassin, B., 2006. "Application of MCMC–GSA model calibration method to urban runoff quality modeling," Reliability Engineering and System Safety, Elsevier, vol. 91(10), pages 1398-1405.
    5. Xu, C. & Gertner, G., 2007. "Extending a global sensitivity analysis technique to models with correlated parameters," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 5579-5590, August.
    6. Saltelli, A. & Andres, T. H. & Homma, T., 1995. "Sensitivity analysis of model output. Performance of the iterated fractional factorial design method," Computational Statistics & Data Analysis, Elsevier, vol. 20(4), pages 387-407, October.
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    Cited by:

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    3. Becker, William, 2020. "Metafunctions for benchmarking in sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    4. Xu, Chonggang & Gertner, George Z., 2009. "Uncertainty analysis of transient population dynamics," Ecological Modelling, Elsevier, vol. 220(3), pages 283-293.
    5. Wei, Pengfei & Lu, Zhenzhou & Song, Jingwen, 2015. "Variable importance analysis: A comprehensive review," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 399-432.
    6. Cheng, Lei & Lu, Zhenzhou & Zhang, Leigang, 2015. "Application of Rejection Sampling based methodology to variance based parametric sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 9-18.
    7. Xu, Chonggang & Gertner, George, 2011. "Understanding and comparisons of different sampling approaches for the Fourier Amplitudes Sensitivity Test (FAST)," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 184-198, January.
    8. Wei, Pengfei & Lu, Zhenzhou & Ruan, Wenbin & Song, Jingwen, 2014. "Regional sensitivity analysis using revised mean and variance ratio functions," Reliability Engineering and System Safety, Elsevier, vol. 121(C), pages 121-135.
    9. Mara, Thierry A. & Tarantola, Stefano, 2012. "Variance-based sensitivity indices for models with dependent inputs," Reliability Engineering and System Safety, Elsevier, vol. 107(C), pages 115-121.
    10. Mara, Thierry Alex, 2009. "Extension of the RBD-FAST method to the computation of global sensitivity indices," Reliability Engineering and System Safety, Elsevier, vol. 94(8), pages 1274-1281.
    11. Zhai, Qingqing & Yang, Jun & Zhao, Yu, 2014. "Space-partition method for the variance-based sensitivity analysis: Optimal partition scheme and comparative study," Reliability Engineering and System Safety, Elsevier, vol. 131(C), pages 66-82.
    12. Pesenti, Silvana M. & Millossovich, Pietro & Tsanakas, Andreas, 2019. "Reverse sensitivity testing: What does it take to break the model?," European Journal of Operational Research, Elsevier, vol. 274(2), pages 654-670.

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