The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Implementation and evaluation of nonparametric regression procedures for sensitivity analysis of computationally demanding models
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Citations
Cites
Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482
Greedy function approximation: A gradient boosting machine
Friedman, J. H. (2001) · The Annals of Statistics · doi:10.1214/aos/1013203451 · Cited by 30,731
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
Gaussian Processes for Machine Learning
Rasmussen, C. E., & Williams, C. K. I. (2005) · doi:10.7551/mitpress/3206.001.0001 · Cited by 11,705
A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection
Kohavi, R. (1995) · Zenodo · doi:10.5281/zenodo.19712698 · Cited by 10,909
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
The Strength of Weak Learnability
Schapire, R. E. (1990) · Machine Learning · doi:10.1007/bf00116037 · Cited by 4,600
Bayesian calibration of computer models
Kennedy, M. C., & O'Hagan, A. (2001) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/1467-9868.00294 · Cited by 4,222
Spline Models for Observational Data
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Cited by
Saltelli, A., Annoni, P., Azzini, I., Campolongo, F., Ratto, M., & Tarantola, S. (2010) · Computer Physics Communications · doi:10.1016/j.cpc.2009.09.018 · Cited by 3,623
Review of surrogate modeling in water resources
Razavi, S., Tolson, B. A., & Burn, D. H. (2012) · Water Resources Research · doi:10.1029/2011wr011527 · Cited by 983
Song, X., Zhang, J., Zhan, C., Xuan, Y., Ye, M., & Xu, C. (2015) · Journal of Hydrology · doi:10.1016/j.jhydrol.2015.02.013 · Cited by 654
Data-driven uncertainty quantification using the arbitrary polynomial chaos expansion
Oladyshkin, S., & Nowak, W. (2012) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2012.05.002 · Cited by 451
POLYNOMIAL-CHAOS-BASED KRIGING
Schobi, R., Sudret, B., & Wiart, J. (2015) · International Journal for Uncertainty Quantification · doi:10.1615/int.j.uncertaintyquantification.2015012467 · Cited by 398
Metadata
- Authors
- Storlie, C. B., Swiler, L. P., Helton, J. C., & Sallaberry, C. J.
- Venue
- Reliability Engineering & System Safety
- Publisher
- Elsevier BV
- Published
- 2009-11
- Volume
- 94
- Issue
- 11
- Pages
- 1735-1763
- ISSN
- 0951-8320 (ISSN-L)
- OpenAlex
- W2065775831
- Cited by
- 327
- References
- 12
- FWCI
- 31.05
- Type
- journal article
- Language
- en
- Open access
- closed
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- Storlie, C. B., Swiler, L. P., Helton, J. C., & Sallaberry, C. J. (2009). Implementation and evaluation of nonparametric regression procedures for sensitivity analysis of computationally demanding models. Reliability Engineering & System Safety. https://doi.org/10.1016/j.ress.2009.05.007
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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.
