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.
Keywords
Curve fittingSmoothingComputer scienceFunction (biology)Limit (mathematics)Selection (genetic algorithm)Model selectionOptimal designMathematical optimizationBayesian probabilityRegressionRegression analysisMathematicsStatisticsMachine learningArtificial intelligence
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Publication Info
- Year
- 1978
- Type
- article
- Volume
- 40
- Issue
- 1
- Pages
- 1-24
- Citations
- 656
- Access
- Closed
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Cite This
Anthony O’Hagan
(1978).
Curve Fitting and Optimal Design for Prediction.
Journal of the Royal Statistical Society Series B (Statistical Methodology)
, 40
(1)
, 1-24.
https://doi.org/10.1111/j.2517-6161.1978.tb01643.x
Identifiers
- DOI
- 10.1111/j.2517-6161.1978.tb01643.x