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Quantile based global sensitivity measures

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  • Kucherenko, Sergei
  • Song, Shufang
  • Wang, Lu

Abstract

New global sensitivity measures based on quantiles of the output are introduced. Such measures can be used for global sensitivity analysis of problems in which αth quantiles are explicitly the functions of interest and for identification of variables which are the most important in achieving extreme values of the model output. It is proven that there is a link between introduced measures and Sobol’ main effect sensitivity indices. Two different Monte Carlo estimators are considered. It is shown that the double loop reordering approach is much more efficient than the brute force estimator. Several test cases and practical case studies related to structural safety are used to illustrate the developed method. Results of numerical calculations show the efficiency of the presented technique.

Suggested Citation

  • Kucherenko, Sergei & Song, Shufang & Wang, Lu, 2019. "Quantile based global sensitivity measures," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 35-48.
    • Handle: RePEc:eee:reensy:v:185:y:2019:i:c:p:35-48
    DOI: 10.1016/j.ress.2018.12.001
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    References listed on IDEAS

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    Cited by:

    1. Song, Shufang & Bai, Zhiwei & Kucherenko, Sergei & Wang, Lu & Yang, Caiqiong, 2021. "Quantile sensitivity measures based on subset simulation importance sampling," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    2. Straub, Daniel & Ehre, Max & Papaioannou, Iason, 2022. "Decision-theoretic reliability sensitivity," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    3. Torii, André Jacomel & Novotny, Antonio André, 2021. "A priori error estimates for local reliability-based sensitivity analysis with Monte Carlo Simulation," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    4. Nikishova, Anna & Comi, Giovanni E. & Hoekstra, Alfons G., 2020. "Sensitivity analysis based dimension reduction of multiscale models," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 170(C), pages 205-220.
    5. Marrel, Amandine & Chabridon, Vincent, 2021. "Statistical developments for target and conditional sensitivity analysis: Application on safety studies for nuclear reactor," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    6. Zhou, Changcong & Shi, Zhuangke & Kucherenko, Sergei & Zhao, Haodong, 2022. "A unified approach for global sensitivity analysis based on active subspace and Kriging," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    7. Uoseph Hamdi Salemi & Esmaile Khorram & Yuancheng Si & Saralees Nadarajah, 2020. "Sensitivity analysis of censoring schemes in progressively type-II right censored order statistics," OPSEARCH, Springer;Operational Research Society of India, vol. 57(1), pages 163-189, March.
    8. Zdeněk Kala, 2021. "New Importance Measures Based on Failure Probability in Global Sensitivity Analysis of Reliability," Mathematics, MDPI, vol. 9(19), pages 1-20, September.

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