The theory and application of penalized methods or reproducing kernel Hilbert spaces made easy

DOI10.1214/12-SS101zbMath1302.62165arXiv1111.1915MaRDI QIDQ1951653

Nancy E. Heckman

Publication date: 24 May 2013

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

Full work available at URL: https://arxiv.org/abs/1111.1915

zbMATH Keywords

splinesreproducing kernel Hilbert spacepenalized likelihood

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Ridge regression; shrinkage estimators (Lasso) (62J07) Hilbert spaces with reproducing kernels (= (proper) functional Hilbert spaces, including de Branges-Rovnyak and other structured spaces) (46E22)

Related Items (3)

Switching nonparametric regression models ⋮ Linearly Constrained Linear Quadratic Regulator from the Viewpoint of Kernel Methods ⋮ Algorithm 1023: Restoration of Function by Integrals with Cubic Integral Smoothing Spline in R

Uses Software

• fda (R)
• ElemStatLearn

Cites Work

• Inference for functional data with applications
• Smoothing by spline functions. II
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• Some results on Tchebycheffian spline functions and stochastic processes
• Cross-Validated Spline Methods for the Estimation of Three-Dimensional Tumor Size Distributions from Observations on Two-Dimensional Cross Sections
• Penalized regression with model-based penalties
• Nonparametric spline regression with prior information
• Theory of Reproducing Kernels
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