Monte Carlo estimators of first-and total-orders Sobol' indices
Abstract
This study compares the performances of two sampling-based strategies for the simultaneous estimation of the first-and total-orders variance-based sensitivity indices (a.k.a Sobol' indices). The first strategy was introduced by [8] and is the current approach employed by practitioners. The second one was only recently introduced by the authors of the present article. They both rely on different estimators of first-and total-orders Sobol' indices. The asymp-totic normal variances of the two sets of estimators are established and their accuracies are compared theoretically and numerically. The results show that the new strategy outperforms the current one.Keywords: global sensitivity analysis, variance-based sensitivity indices, first-order Sobol' index, total-order Sobol' index, Monte Carlo estimate, asymptotic normality
Keywords
Cite
@article{arxiv.2006.08232,
title = {Monte Carlo estimators of first-and total-orders Sobol' indices},
author = {Ivano Azzini and Thierry Mara and Rossana Rosati},
journal= {arXiv preprint arXiv:2006.08232},
year = {2020}
}