Global Sensitivity Analysis: a novel generation of mighty estimators based on rank statistics
Résumé
We propose a new statistical estimation framework for a large family of global
sensitivity analysis indices. Our approach is based on rank statistics and uses an
empirical correlation coefficient recently introduced by Chatterjee [9]. We show how
to apply this approach to compute not only the Cramér-von-Mises indices, which
are directly related to Chatterjee’s notion of correlation, but also first-order Sobol
indices, general metric space indices and higher-order moment indices. We establish
consistency of the resulting estimators and demonstrate their numerical efficiency,
especially for small sample sizes. In addition, we prove a central limit theorem for
the estimators of the first-order Sobol indices.
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New-Look_9_hal.pdf (788.93 Ko)
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NVU-QoI-CVM.png (207.76 Ko)
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New_Look_Bernoulli_REV3_corr_erratum_HAL.pdf (213.46 Ko)
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boxplot_both_mse_1_gfonction.png (4.63 Ko)
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gfonction_var.png (27.67 Ko)
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results_comparison_resized.png (70.91 Ko)
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| Origine | Fichiers produits par l'(les) auteur(s) |
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| Origine | Fichiers produits par l'(les) auteur(s) |
|---|