The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Sensitivity analysis of model output: variance-based methods make the difference
Open accessAbstract
This paper is intended to review a number of variancebased methods used in Sensitivity Analysis (SA) to ascertain how much a model (numerical or otherwise) depends on each or some of its input parameters.A class of variance-baaed methods (correlation ratio or importance measure) that is capable of measuring only the main effect contribution of each input parameter on the output variance are described briefly.In addition, two methods (Sobol' and FAST) that are capable of computing the so-called "Total Sensitivity Indices" (TSI), which measures a parameter's main effect and all the interactions (of any order) involving that parameter, are described in details.An illustrated example demonstrates that the incorporation of total effect indices is the only way to perform a rigorous quantitative sensitivity analysis.
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Citations
Cites
A Quantitative Model-Independent Method for Global Sensitivity Analysis of Model Output
Saltelli, A., Tarantola, S., & Chan, K. P.-S. (1999) · Technometrics · doi:10.1080/00401706.1999.10485594 · Cited by 2,275
Self-citationThe Use of the Rank Transform in Regression
Iman, R. L., & Conover, W. J. (1979) · Technometrics · doi:10.1080/00401706.1979.10489820 · Cited by 514
Cited by
Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems
Helton, J. C., & Davis, F. J. (2003) · Reliability Engineering & System Safety · doi:10.1016/s0951-8320(03)00058-9 · Cited by 2,688
Making best use of model evaluations to compute sensitivity indices
Saltelli, A. (2002) · Computer Physics Communications · doi:10.1016/s0010-4655(02)00280-1 · Cited by 2,228
Self-citationSurvey of sampling-based methods for uncertainty and sensitivity analysis
Helton, J. C., Johnson, J. D., Sallaberry, C. J., & Storlie, C. B. (2006) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2005.11.017 · Cited by 1,237
Zhang, X., Trame, M., Lesko, L., & Schmidt, S. (2015) · CPT: Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.6 · Cited by 467
Saltelli, A., & Tarantola, S. (2002) · Journal of the American Statistical Association · doi:10.1198/016214502388618447 · Cited by 447
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Metadata
- Authors
- Chan, K., Saltelli, A., & Tarantola, S.
- Venue
- Proceedings of the 29th conference on Winter simulation - WSC '97
- Publisher
- ACM Press
- Published
- 1997
- Pages
- 261-268
- ISSN
- 0891-7736 (ISSN-L)
- OpenAlex
- W2011470199
- Cited by
- 313
- References
- 2
- FWCI
- 41.60
- Type
- conference paper
- Language
- en
- Open access
- bronze
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- Chan, K., Saltelli, A., & Tarantola, S. (1997). Sensitivity analysis of model output: variance-based methods make the difference. Proceedings of the 29th conference on Winter simulation - WSC '97. https://doi.org/10.1145/268437.268489
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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