The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Curve Fitting and Optimal Design for Prediction
Abstract
Summary: The optimal design problem is tackled in the framework of a new model and new objectives. A regression model is proposed in which the regression function is permitted to take any form over the space X of independent variables. The design objective is based on fitting a simplified function for prediction. The approach is Bayesian throughout. The new designs are more robust than conventional ones. They also avoid the need to limit artificially design points to a predetermined subset of X. New solutions are also offered for the problems of smoothing, curve fitting and the selection of regressor variables.
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Citations
Cites
Bayes Estimates for the Linear Model
Lindley, D. V., & Smith, A. F. M. (1972) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1972.tb00885.x · Cited by 2,025
Cited by
Gaussian Processes for Machine Learning
Rasmussen, C. E., & Williams, C. K. I. (2005) · doi:10.7551/mitpress/3206.001.0001 · Cited by 11,705
“This idea was proposed under the name of Bayes-Hermite quadrature by O’Hagan [1991], and later under the name of Bayesian Monte Carlo in Rasmussen and Ghahramani [2003].”
Rue, H., Martino, S., & Chopin, N. (2009) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2008.00700.x · Cited by 5,279
Hastie, T., & Tibshirani, R. (1993) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1993.tb01939.x · Cited by 1,902
Flood inundation modelling: A review of methods, recent advances and uncertainty analysis
Teng, J., Jakeman, A. J., Vaze, J., Croke, B. F. W., Dutta, D., & Kim, S. (2017) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2017.01.006 · Cited by 1,456
Computer Model Calibration Using High-Dimensional Output
Higdon, D., Gattiker, J., Williams, B., & Rightley, M. (2008) · Journal of the American Statistical Association · doi:10.1198/016214507000000888 · Cited by 867
Metadata
- Authors
- O'Hagan, A.
- Venue
- Journal of the Royal Statistical Society Series B: Statistical Methodology
- Publisher
- Oxford University Press (OUP)
- Published
- 1978-09-01
- Volume
- 40
- Issue
- 1
- Pages
- 1-24
- ISSN
- 1369-7412 (ISSN-L)
- 1467-9868
- OpenAlex
- W2904816695
- Cited by
- 661
- References
- 1
- FWCI
- 3.51
- Type
- journal article
- Language
- en
- Open access
- closed
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- O'Hagan, A. (1978). Curve Fitting and Optimal Design for Prediction. Journal of the Royal Statistical Society Series B: Statistical Methodology. https://doi.org/10.1111/j.2517-6161.1978.tb01643.x
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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.
