Yang, Jiannan orcid.org/0000-0001-8323-7406 (2023) Derivative based global sensitivity analysis and its entropic link. [Preprint]
Abstract
Distribution-based global sensitivity analysis (GSA), such as variance-based and entropy-based approaches, can provide quantitative sensitivity information. However, they can be expensive to evaluate and are thus limited to low dimensional problems. Derivative-based GSA, on the other hand, require much fewer model evaluations. It is known that derivative-based GSA is closely linked to variance-based total sensitivity index, while its relationship with the entropy-based measure is unclear. To fill this gap, we introduce a log-derivative based functional to demonstrate that the entropy-based and derivative-based sensitivity measures are strongly connected. In particular, we give proofs that, similar to the case with variance-based GSA, there is an inequality relationship between entropy-based and derivative-based important measures. Both analytical and numerical verifications are provided. Examples show that the derivative-based methods give similar variable rankings as entropy-based index and can thus be potentially used as a proxy for both variance-based and entropy-based distribution-type GSA.
Metadata
Item Type: | Preprint |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | 11 page, 3 figures, 4 tables |
Keywords: | math.NA,cs.NA,math.PR,stat.CO |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Physics (York) |
Depositing User: | Pure (York) |
Date Deposited: | 05 Jan 2024 10:20 |
Last Modified: | 21 Jan 2025 18:31 |
Status: | Published |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:207157 |