Variance reduction for estimation of Shapley effects and adaptation to unknown input distribution
Statistics Theory
2020-02-14 v2 Statistics Theory
Abstract
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 and suggest a new one with a lower variance. Then, when the distribution of the inputs is unknown, we extend these estimators. Finally, we provide asymptotic properties of the estimators studied in this article.
Keywords
Cite
@article{arxiv.1812.09168,
title = {Variance reduction for estimation of Shapley effects and adaptation to unknown input distribution},
author = {Baptiste Broto and François Bachoc and Marine Depecker},
journal= {arXiv preprint arXiv:1812.09168},
year = {2020}
}