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Variance-based sensitivity analysis: The quest for better estimators and designs between explorativity and economy

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  • Lo Piano, Samuele
  • Ferretti, Federico
  • Puy, Arnald
  • Albrecht, Daniel
  • Saltelli, Andrea

Abstract

Variance-based sensitivity indices have established themselves as a reference amongst practitioners of sensitivity analysis of model outputs. A variance-based sensitivity analysis typically produces the first-order sensitivity indices Sj and the so-called total-effect sensitivity indices Tj for the uncertain factors of the mathematical model under analysis.

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  • Lo Piano, Samuele & Ferretti, Federico & Puy, Arnald & Albrecht, Daniel & Saltelli, Andrea, 2021. "Variance-based sensitivity analysis: The quest for better estimators and designs between explorativity and economy," Reliability Engineering and System Safety, Elsevier, vol. 206(C).
    • Handle: RePEc:eee:reensy:v:206:y:2021:i:c:s0951832020307961
    DOI: 10.1016/j.ress.2020.107300
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    References listed on IDEAS

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    6. Sobol′ , I.M, 2001. "Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 55(1), pages 271-280.
    7. 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.
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    Cited by:

    1. 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).
    2. Ye, Dongwei & Nikishova, Anna & Veen, Lourens & Zun, Pavel & Hoekstra, Alfons G., 2021. "Non-intrusive and semi-intrusive uncertainty quantification of a multiscale in-stent restenosis model," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    3. Pashanejad, Ehsan & Thierry, Hugo & Robinson, Brian E. & Parrott, Lael, 2023. "The application of semantic modelling to map pollination service provisioning at large landscape scales," Ecological Modelling, Elsevier, vol. 484(C).
    4. Xiang Peng & Xiaoqing Xu & Jiquan Li & Shaofei Jiang, 2021. "A Sampling-Based Sensitivity Analysis Method Considering the Uncertainties of Input Variables and Their Distribution Parameters," Mathematics, MDPI, vol. 9(10), pages 1-18, May.
    5. 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).

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