Making robust the cross-validatory choice of smoothing parameter in spline smoothing regression
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Publication:3473171
DOI10.1080/03610928908829916zbMath0696.62182OpenAlexW1989180903WikidataQ57194395 ScholiaQ57194395MaRDI QIDQ3473171
Publication date: 1989
Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/03610928908829916
Related Items (12)
Unnamed Item ⋮ Performance of Robust GCV and Modified GCV for Spline Smoothing ⋮ Dependent error regression smoothing: A new method and PC program ⋮ Robust GCV choice of the regularization parameter for correlated data ⋮ Comparing parameter choice methods for regularization of ill-posed problems ⋮ Efficient algorithms for robust generalized cross-validation spline smoothing ⋮ Practical use of robust GCV and modified GCV for spline smoothing ⋮ The smoothing parameter, confidence interval and robustness for smoothing splines ⋮ A statistical approach to case based reasoning, with application to breast cancer data ⋮ PCA-based dimension reduction for splines ⋮ Parameter Choices for Fast Harmonic Spline Approximation ⋮ Reference priors for shrinkage and smoothing parameters
Cites Work
- Sur le choix du paramètre d'ajustement dans le lissage par fonctions spline
- Bandwidth choice for nonparametric regression
- The hat matrix for smoothing splines
- Smoothing noisy data with spline functions: Estimating the correct degree of smoothing by the method of generalized cross-validation
- Statistical predictor identification
- An optimal selection of regression variables
- Mean Square Error of Prediction as a Criterion for Selecting Variables
- Smoothing noisy data with spline functions
- Smoothing noisy data with spline functions
- A new look at the statistical model identification
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