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Global sensitivity analysis: a generalized, unbiased and optimal estimator of total-effect variance

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  • Matieyendou Lamboni

    (Department DFRST, University of Guyane
    228-UMR Espace-Dev
    Institute for Environment and Sustainability, EC-Joint Research Centre)

Abstract

Variance-based sensitivity analysis and multivariate sensitivity analysis aim to apportion the variability of the model output(s) into input factors and their interactions. Sobol’s total index, which accounts for the effects of interactions, serves as a practical tool to deal with the curse of dimensionality. In this paper, we address the problem of the efficient estimation of Sobol’s total index. First, we provide a generalized and optimal estimator of the variance of the total effect function, including Jansen’s estimator, its rate of convergence, and its asymptotic distribution; second, we derive the asymptotic distribution of total indices; and third, we investigate the applicability of these results to allow for improving the estimation of the total indices for some specific degrees of the kernel.

Suggested Citation

  • Matieyendou Lamboni, 2018. "Global sensitivity analysis: a generalized, unbiased and optimal estimator of total-effect variance," Statistical Papers, Springer, vol. 59(1), pages 361-386, March.
    • Handle: RePEc:spr:stpapr:v:59:y:2018:i:1:d:10.1007_s00362-016-0768-5
    DOI: 10.1007/s00362-016-0768-5
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    References listed on IDEAS

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    Cited by:

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    3. Liu, Fuchao & Wei, Pengfei & Tang, Chenghu & Wang, Pan & Yue, Zhufeng, 2019. "Global sensitivity analysis for multivariate outputs based on multiple response Gaussian process model," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 287-298.

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