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Convergence of sensitivity analysis methods for evaluating combined influences of model inputs

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  • Awad, Majdi
  • Senga Kiesse, Tristan
  • Assaghir, Zainab
  • Ventura, Anne

Abstract

This work aims at studying Morris’ extension method to evaluate the contribution of combined variations of inputs to variations of a model output. There is a lack of studies on the Morris’ extension method concerning crucial choices of the adequate number of trajectories to distinguish influential and non-influential groups of pairs of inputs, rank pairs of inputs according to their relative importance and reach out the stability of sensitivity indices values. The Morris’ extension method was studied regarding the three previous issues via applications on simple and complex models, in comparison with total interaction indices of Sobol. Formal criteria were implemented to assess the convergence of sensitivity analysis results. Sensitivity indices based on the median of mixed elementary effects (MEE) were investigated and found to be competing with classical ones based on the mean of MEE, to achieve convergent results.

Suggested Citation

  • Awad, Majdi & Senga Kiesse, Tristan & Assaghir, Zainab & Ventura, Anne, 2019. "Convergence of sensitivity analysis methods for evaluating combined influences of model inputs," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 109-122.
  • Handle: RePEc:eee:reensy:v:189:y:2019:i:c:p:109-122
    DOI: 10.1016/j.ress.2019.03.050
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    References listed on IDEAS

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    1. Zio, E., 2009. "Reliability engineering: Old problems and new challenges," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 125-141.
    2. Kucherenko, S. & Rodriguez-Fernandez, M. & Pantelides, C. & Shah, N., 2009. "Monte Carlo evaluation of derivative-based global sensitivity measures," Reliability Engineering and System Safety, Elsevier, vol. 94(7), pages 1135-1148.
    3. Konakli, Katerina & Sudret, Bruno, 2016. "Global sensitivity analysis using low-rank tensor approximations," Reliability Engineering and System Safety, Elsevier, vol. 156(C), pages 64-83.
    4. Barry Anderson & Emanuele Borgonovo & Marzio Galeotti & Roberto Roson, 2014. "Uncertainty in Climate Change Modeling: Can Global Sensitivity Analysis Be of Help?," Risk Analysis, John Wiley & Sons, vol. 34(2), pages 271-293, February.
    5. Liu, Qiao & Homma, Toshimitsu, 2009. "A new computational method of a moment-independent uncertainty importance measure," Reliability Engineering and System Safety, Elsevier, vol. 94(7), pages 1205-1211.
    6. 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.
    7. Liu, Ruixue & Owen, Art B., 2006. "Estimating Mean Dimensionality of Analysis of Variance Decompositions," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 712-721, June.
    8. Wang, Pan & Lu, Zhenzhou & Ren, Bo & Cheng, Lei, 2013. "The derivative based variance sensitivity analysis for the distribution parameters and its computation," Reliability Engineering and System Safety, Elsevier, vol. 119(C), pages 305-315.
    9. Andrianandraina & Anne Ventura & Tristan Senga Kiessé & Bogdan Cazacliu & Rachida Idir & Hayo M. G. Werf, 2015. "Sensitivity Analysis of Environmental Process Modeling in a Life Cycle Context: A Case Study of Hemp Crop Production," Journal of Industrial Ecology, Yale University, vol. 19(6), pages 978-993, December.
    10. Chakraborty, Souvik & Chowdhury, Rajib, 2017. "A hybrid approach for global sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 158(C), pages 50-57.
    11. Siti Nuryanah & Sardar M. N. Islam, 2015. "The Context of the Case Study," Palgrave Macmillan Books, in: Corporate Governance and Financial Management, chapter 5, pages 145-156, Palgrave Macmillan.
    12. Sudret, Bruno, 2008. "Global sensitivity analysis using polynomial chaos expansions," Reliability Engineering and System Safety, Elsevier, vol. 93(7), pages 964-979.
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    2. Shi, Wen & Zhou, Qing & Zhou, Yanju, 2023. "An efficient elementary effect-based method for sensitivity analysis in identifying main and two-factor interaction effects," Reliability Engineering and System Safety, Elsevier, vol. 237(C).

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