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Mathematics > Statistics Theory

arXiv:1812.09168 (math)
[Submitted on 21 Dec 2018 (v1), last revised 13 Feb 2020 (this version, v2)]

Title:Variance reduction for estimation of Shapley effects and adaptation to unknown input distribution

Authors:Baptiste Broto (CEA), François Bachoc (IMT), Marine Depecker (CEA)
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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.
Subjects: Statistics Theory (math.ST)
Cite as: arXiv:1812.09168 [math.ST]
  (or arXiv:1812.09168v2 [math.ST] for this version)
  https://doi.org/10.48550/arXiv.1812.09168

Submission history

From: Baptiste Broto [view email] [via CCSD proxy]
[v1] Fri, 21 Dec 2018 14:56:43 UTC (73 KB)
[v2] Thu, 13 Feb 2020 09:30:12 UTC (86 KB)
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