The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
How to avoid a perfunctory sensitivity analysis
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Citations
Cites
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
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
Self-citationOn the distribution of points in a cube and the approximate evaluation of integrals
Sobol', I. M. (1967) · USSR Computational Mathematics and Mathematical Physics · doi:10.1016/0041-5553(67)90144-9 · Cited by 2,975
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-citationQuantification of modelling uncertainties in a large ensemble of climate change simulations.
Murphy, J. M., Sexton, D. M. H., Barnett, D. N., Jones, G. S., Webb, M. J., Collins, M., & Stainforth, D. A. (2004) · Nature · doi:10.1038/nature02771 · Cited by 1,655
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
Characterising performance of environmental models
Bennett, N. D., Croke, B. F. W., Guariso, G., Guillaume, J. H. A., Hamilton, S. H., Jakeman, A. J., Marsili-Libelli, S., Newham, L. T. H., Norton, J. P., Perrin, C., et al. (2013) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2012.09.011 · Cited by 1,536
Understanding the dominant controls on litter decomposition
Bradford, M. A., Berg, B., Maynard, D. S., Wieder, W. R., & Wood, S. A. (2016) · Journal of Ecology · doi:10.1111/1365-2745.12507 · Cited by 669
Good practice in Bayesian network modelling
Chen, S. H., & Pollino, C. A. (2012) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2012.03.012 · Cited by 657
AquaCrop: FAO's crop water productivity and yield response model
Vanuytrecht, E., Raes, D., Steduto, P., Hsiao, T. C., Fereres, E., Heng, L. K., Garcia Vila, M., & Mejias Moreno, P. (2014) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2014.08.005 · Cited by 403
Toward SALib 2.0: Advancing the accessibility and interpretability of global sensitivity analyses
Iwanaga, T., Usher, W., & Herman, J. (2022) · Socio-Environmental Systems Modelling · doi:10.18174/sesmo.18155 · Cited by 367
Metadata
- Authors
- Saltelli, A., & Annoni, P.
- Venue
- Environmental Modelling & Software
- Publisher
- Elsevier BV
- Published
- 2010-12
- Volume
- 25
- Issue
- 12
- Pages
- 1508-1517
- ISSN
- 1364-8152 (ISSN-L)
- OpenAlex
- W2062075925
- Cited by
- 974
- References
- 7
- FWCI
- 37.71
- Type
- journal article
- Language
- en
- Open access
- closed
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- Saltelli, A., & Annoni, P. (2010). How to avoid a perfunctory sensitivity analysis. Environmental Modelling & Software. https://doi.org/10.1016/j.envsoft.2010.04.012
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
