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https://dx.doi.org/10.48550/ar...
Article . 2024
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A new paradigm for global sensitivity analysis

Authors: Mazo, Gildas;

A new paradigm for global sensitivity analysis

Abstract

Current theory of global sensitivity analysis, based on a nonlinear functional ANOVA decomposition of the random output, is limited in scope-for instance, the analysis is limited to the output's variance and the inputs have to be mutually independent-and leads to sensitivity indices the interpretation of which is not fully clear, especially interaction effects. Alternatively, sensitivity indices built for arbitrary user-defined importance measures have been proposed but a theory to define interactions in a systematic fashion and/or establish a decomposition of the total importance measure is still missing. It is shown that these important problems are solved all at once by adopting a new paradigm. By partitioning the inputs into those causing the change in the output and those which do not, arbitrary user-defined variability measures are identified with the outcomes of a factorial experiment at two levels, leading to all factorial effects without assuming any functional decomposition. To link various well-known sensitivity indices of the literature (Sobol indices and Shapley effects), weighted factorial effects are studied and utilized.

Keywords

FOS: Computer and information sciences, Computer Science - Machine Learning, 330, [SPI] Engineering Sciences [physics], Machine Learning (stat.ML), interactions, [STAT.OT]Statistics [stat]/Other Statistics [stat.ML], factorial experiment, Machine Learning (cs.LG), Methodology (stat.ME), Statistics - Machine Learning, global sensitivity analysis, [MATH.MATH-ST] Mathematics [math]/Statistics [math.ST], main effects, Sobol indices, Statistics - Methodology

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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