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Moment Independent Importance Measures: New Results and Analytical Test Cases

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  • Emanuele Borgonovo
  • William Castaings
  • Stefano Tarantola

Abstract

Moment independent methods for the sensitivity analysis of model output are attracting growing attention among both academics and practitioners. However, the lack of benchmarks against which to compare numerical strategies forces one to rely on ad hoc experiments in estimating the sensitivity measures. This article introduces a methodology that allows one to obtain moment independent sensitivity measures analytically. We illustrate the procedure by implementing four test cases with different model structures and model input distributions. Numerical experiments are performed at increasing sample size to check convergence of the sensitivity estimates to the analytical values.

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  • Emanuele Borgonovo & William Castaings & Stefano Tarantola, 2011. "Moment Independent Importance Measures: New Results and Analytical Test Cases," Risk Analysis, John Wiley & Sons, vol. 31(3), pages 404-428, March.
    • Handle: RePEc:wly:riskan:v:31:y:2011:i:3:p:404-428
    DOI: 10.1111/j.1539-6924.2010.01519.x
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    3. Elmar Plischke & Emanuele Borgonovo, 2020. "Fighting the Curse of Sparsity: Probabilistic Sensitivity Measures From Cumulative Distribution Functions," Risk Analysis, John Wiley & Sons, vol. 40(12), pages 2639-2660, December.
    4. Tatsuya Sakurahara & Seyed Reihani & Ernie Kee & Zahra Mohaghegh, 2020. "Global importance measure methodology for integrated probabilistic risk assessment," Journal of Risk and Reliability, , vol. 234(2), pages 377-396, April.
    5. Shirin Karimi & Bahman Jabbarian Amiri & Arash Malekian, 2019. "Similarity Metrics-Based Uncertainty Analysis of River Water Quality Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(6), pages 1927-1945, April.
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    11. Ibsen Chivatá Cárdenas & Saad S.H. Al‐Jibouri & Johannes I.M. Halman & Frits A. van Tol, 2014. "Modeling Risk‐Related Knowledge in Tunneling Projects," Risk Analysis, John Wiley & Sons, vol. 34(2), pages 323-339, February.
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    13. Song, Yupeng & Sun, Tao & Zhang, Zili, 2023. "Fatigue reliability analysis of floating offshore wind turbines considering the uncertainty due to finite sampling of load conditions," Renewable Energy, Elsevier, vol. 212(C), pages 570-588.
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    15. Limao Zhang & Xianguo Wu & Yawei Qin & Miroslaw J. Skibniewski & Wenli Liu, 2016. "Towards a Fuzzy Bayesian Network Based Approach for Safety Risk Analysis of Tunnel‐Induced Pipeline Damage," Risk Analysis, John Wiley & Sons, vol. 36(2), pages 278-301, February.
    16. 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.
    17. Yishang Zhang & Yongshou Liu & Xufeng Yang & Bin Zhao, 2015. "An efficient Kriging method for global sensitivity of structural reliability analysis with non-probabilistic convex model," Journal of Risk and Reliability, , vol. 229(5), pages 442-455, October.
    18. Zdeněk Kala, 2020. "Sensitivity Analysis in Probabilistic Structural Design: A Comparison of Selected Techniques," Sustainability, MDPI, vol. 12(11), pages 1-19, June.
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