Kernel spline regression

DOI10.1002/cjs.5550330207zbMath1071.62034OpenAlexW2151784021MaRDI QIDQ5696347

W. John Braun, Li-Shan Huang

Publication date: 18 October 2005

Published in: Canadian Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/cjs.5550330207

zbMATH Keywords

bandwidthknotskernel regressionAkaike information criterionp-splines

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Numerical computation using splines (65D07)

Related Items (2)

Optimal convergence rates, Bahadur representation, and asymptotic normality of partitioning estimators ⋮ Asymptotics for \(L_1\)-wavelet method for nonparametric regression

Uses Software

• R

Cites Work

• Optimal bandwidth selection in nonparametric regression function estimation
• Rates of convergence for the pre-asymptotic substitution bandwidth selector
• Flexible smoothing with \(B\)-splines and penalties. With comments and a rejoinder by the authors
• Smoothing with Split Linear Fits
• An Effective Bandwidth Selector for Local Least Squares Regression
• Smoothing Parameter Selection in Nonparametric Regression Using an Improved Akaike Information Criterion
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