A comprehensive comparison of total-order estimators for global sensitivity analysis

@inproceedings{Puy2021ACC,
  title={A comprehensive comparison of total-order estimators for global sensitivity analysis},
  author={A. Puy and W. Becker and S. L. Piano and A. Saltelli},
  year={2021}
}
Sensitivity analysis helps identify which model inputs convey the most uncertainty to the model output. One of the most authoritative measures in global sensitivity analysis is the Sobol’ total-order index, which can be computed with several different estimators. Although previous comparisons exist, it is hard to know which estimator performs best since the results are contingent on the benchmark setting defined by the analyst (the sampling method, the distribution of the model inputs, the… Expand

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References

SHOWING 1-10 OF 51 REFERENCES
Variance-based sensitivity analysis: The quest for better estimators and designs between explorativity and economy
Abstract Variance-based sensitivity indices have established themselves as a reference among practitioners of sensitivity analysis of model outputs. A variance-based sensitivity analysis typicallyExpand
Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index
TLDR
Existing and new practices for sensitivity analysis of model output are compared and recommendations on which to use are offered to help practitioners choose which techniques to use. Expand
A Review on Global Sensitivity Analysis Methods
This chapter makes a review, in a complete methodological framework, of various global sensitivity analysis methods of model output. Numerous statistical and probabilistic tools (regression,Expand
Importance measures in global sensitivity analysis of nonlinear models
Abstract The present paper deals with a new method of global sensitivity analysis of nonlinear models. This is based on a measure of importance to calculate the fractional contribution of the inputExpand
Metafunctions for benchmarking in sensitivity analysis
  • W. Becker
  • Computer Science
  • Reliab. Eng. Syst. Saf.
  • 2020
TLDR
A flexible ‘metafunction’ framework to benchmarking is introduced which randomly generates test problems of varying dimensionality and functional form using random combinations of plausible basis functions, and a range of sample sizes. Expand
A simple and efficient method for global sensitivity analysis based on cumulative distribution functions
TLDR
A novel GSA method, called PAWN, to efficiently compute density-based sensitivity indices, which is to characterise output distributions by their Cumulative Distribution Functions (CDF), which are easier to derive than PDFs. Expand
A new framework for comprehensive, robust, and efficient global sensitivity analysis: 1. Theory
Computer simulation models are continually growing in complexity with increasingly more factors to be identified. Sensitivity Analysis (SA) provides an essential means for understanding the role andExpand
Distribution-based sensitivity analysis from a generic input-output sample
TLDR
An alternative approximation procedure is presented that makes PAWN applicable to a generic sample of inputs and outputs while requiring only one tuning parameter and allows the user to estimate PAWN indices as complementary metrics in multi-method GSA applications without additional computational cost. Expand
Sensitivity analysis of a sensitivity analysis: We are likely overlooking the impact of distributional assumptions
Although uncertainty in input factor distributions is known to affect sensitivity analysis (SA) results, a standard procedure to quantify its impact is not available. We addressed this problem byExpand
Monte Carlo estimators of first-and total-orders Sobol' indices
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). TheExpand
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