Low-rank approximation for smoothing spline via eigensystem truncation
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Publication:6541765
DOI10.1002/sta4.355MaRDI QIDQ6541765
Publication date: 21 May 2024
Published in: Stat (Search for Journal in Brave)
algorithmssmoothingnonparametric regressionstatistical computingcomputationally intensive methodslarge and complex data sets
Cites Work
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- Approximation of eigenfunctions in kernel-based spaces
- Sampled forms of functional PCA in reproducing kernel Hilbert spaces
- Spline spaces are optimal for \(L^2\) n-width
- Randomized sketches for kernels: fast and optimal nonparametric regression
- Orthogonal Series Density Estimation and the Kernel Eigenvalue Problem
- Smoothing Splines
- 10.1162/15324430260185619
- Divide and Recombine Approaches for Fitting Smoothing Spline Models with Large Datasets
- Efficient computation of smoothing splines via adaptive basis sampling
- Newton-Raphson and EM Algorithms for Linear Mixed-Effects Models for Repeated-Measures Data
- Mixed Effects Smoothing Spline Analysis of Variance
- Smoothing Spline Gaussian Regression: More Scalable Computation via Efficient Approximation
- Thin Plate Regression Splines
- Smoothing Spline ANOVA Models
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