Efficient computation of smoothing splines via adaptive basis sampling

DOI10.1093/biomet/asv009zbMath1452.62286OpenAlexW1908719465MaRDI QIDQ3455811

Ping Ma, Nan Zhang, Jianhua Z. Huang

Publication date: 11 December 2015

Published in: Biometrika (Search for Journal in Brave)

Full work available at URL: https://semanticscholar.org/paper/2f5b2e22578ef53ce5c5606af143c210ecf9204a

zbMATH Keywords

samplingnonparametric regressionreproducing kernel Hilbert spacepenalized least squaresBayesian confidence intervalcore-mantle boundary

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Asymptotic properties of nonparametric inference (62G20) Nonparametric tolerance and confidence regions (62G15)

Related Items (4)

Smoothing Splines Approximation Using Hilbert Curve Basis Selection ⋮ Efficient estimation of variance components in nonparametric mixed-effects models with large samples ⋮ Large dynamic covariance matrix estimation with an application to portfolio allocation: a semiparametric reproducing kernel Hilbert space approach ⋮ Divide and Recombine Approaches for Fitting Smoothing Spline Models with Large Datasets

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