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A Review on Global Sensitivity Analysis Methods
Open accessAbstract
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, smoothing, tests, statistical learning, Monte Carlo, …) aim at determining the model input variables which mostly contribute to an interest quantity depending on model output. This quantity can be for instance the variance of an output variable. Three kinds of methods are distinguished: the screening (coarse sorting of the most influential inputs among a large number), the measures of importance (quantitative sensitivity indices) and the deep exploration of the model behaviour (measuring the effects of inputs on their all variation range). A progressive application methodology is illustrated on a scholar application. A synthesis is given to place every method according to several axes, mainly the cost in number of model evaluations, the model complexity and the nature of brought information.
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Cites
The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162
Global Sensitivity Analysis. The Primer
Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., & Tarantola, S. (2007) · doi:10.1002/9780470725184 · Cited by 6,430
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
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
Identification and Review of Sensitivity Analysis Methods
Christopher Frey, H., & Patil, S. R. (2002) · Risk Analysis · doi:10.1111/0272-4332.00039 · Cited by 1,036
Analysis of variance designs for model output
Jansen, M. J. W. (1999) · Computer Physics Communications · doi:10.1016/s0010-4655(98)00154-4 · Cited by 650
Cited by
A Review of Explainable Artificial Intelligence in Healthcare
Sadeghi, Z., Alizadehsani, R., CIFCI, M. A., Kausar, S., Rehman, R., Mahanta, P., Bora, P. K., Almasri, A., Alkhawaldeh, R. S., Hussain, S., et al. (2024) · Computers and Electrical Engineering · doi:10.1016/j.compeleceng.2024.109370 · Cited by 598
Liu, H., Ong, Y.-S., & Cai, J. (2018) · Structural and Multidisciplinary Optimization · doi:10.1007/s00158-017-1739-8 · Cited by 444
Random forests for global sensitivity analysis: A selective review
Antoniadis, A., Lambert-Lacroix, S., & Poggi, J.-M. (2021) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2020.107312 · Cited by 363
Yondo, R., Andrés, E., & Valero, E. (2018) · Progress in Aerospace Sciences · doi:10.1016/j.paerosci.2017.11.003 · Cited by 311
Yates, J., Daiyan, R., Patterson, R., Egan, R., Amal, R., Ho-Baille, A., & Chang, N. L. (2020) · Cell Reports Physical Science · doi:10.1016/j.xcrp.2020.100209 · Cited by 294
Metadata
- Authors
- Iooss, B., & Lemaître, P.
- Venue
- Operations Research/Computer Science Interfaces Series
- Publisher
- Springer US
- Published
- 2015
- Pages
- 101-122
- ISSN
- 1387-666X (ISSN-L)
- OpenAlex
- W1911106672
- Cited by
- 994
- References
- 6
- FWCI
- 189.75
- Type
- book chapter
- Language
- en
- Open access
- green
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