Practical use of robust GCV and modified GCV for spline smoothing

DOI10.1007/s00180-015-0577-7zbMath1342.65044OpenAlexW2108760780MaRDI QIDQ736591

Robert S. Anderssen, Frank R. de Hoog, Mark A. Lukas

Publication date: 4 August 2016

Published in: Computational Statistics (Search for Journal in Brave)

Full work available at URL: http://researchrepository.murdoch.edu.au/id/eprint/26462/

zbMATH Keywords

prediction errorgeneralized cross-validationsmoothing parameterspline smoothingSobolev error

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Numerical computation using splines (65D07)

Related Items

Performance criteria and discrimination of extreme undersmoothing in nonparametric regression

Uses Software

• gss

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Smoothing by spline functions. II
• Efficient algorithms for robust generalized cross-validation spline smoothing
• The average posterior variance of a smoothing spline and a consistent estimate of the average squared error
• Asymptotic optimality of \(C_ L\) and generalized cross-validation in ridge regression with application to spline smoothing
• Smoothing noisy data with spline functions: Estimating the correct degree of smoothing by the method of generalized cross-validation
• Selection criteria for scatterplot smoothers
• Performance criteria and discrimination of extreme undersmoothing in nonparametric regression
• Smoothing by spline functions.
• A general method for the construction of interpolating or smoothing spline-functions
• Performance of Robust GCV and Modified GCV for Spline Smoothing
• Differentiation of matrix functionals using triangular factorization
• Robust generalized cross-validation for choosing the regularization parameter
• Making robust the cross-validatory choice of smoothing parameter in spline smoothing regression
• Strong robust generalized cross-validation for choosing the regularization parameter
• Smoothing Spline Gaussian Regression: More Scalable Computation via Efficient Approximation
• Confidence Intervals for Nonparametric Curve Estimates
• Estimating the accuracy of (local) cross-validation via randomised GCV choices in kernel or smoothing spline regression
• Smoothing noisy data with spline functions
• Smoothing spline ANOVA models