Variance reduction for estimation of Shapley effects and adaptation to unknown input distribution
Résumé
The Shapley effects are global sensitivity indices: they quantify the impact of each input variable on the output variable in a model. In this work, we suggest new estimators of these sensitivity indices. When the input distribution is known, we investigate the already existing estimator defined in [E. Song, B. L. Nelson, and J. Staum, SIAM/ASA J. Uncertain. Quantif., 4 (2016), pp. 1060--1083] and suggest a new one with a lower variance. Then, when the distribution of the inputs is unknown, we extend these estimators. We provide asymptotic properties of the estimators studied in this article. We also apply one of these estimators to a real data set.
Domaines
Statistiques [math.ST]| Origine | Fichiers produits par l'(les) auteur(s) |
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