Divide and Recombine Approaches for Fitting Smoothing Spline Models with Large Datasets

DOI10.1080/10618600.2017.1402775OpenAlexW2768726407MaRDI QIDQ3391108

Yuedong Wang, Danqing Xu

Publication date: 28 March 2022

Published in: Journal of Computational and Graphical Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/10618600.2017.1402775

zbMATH Keywords

parallel computingscalabilitydivide and conquerspatial adaptivitydebias

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (3)

Smoothing Splines Approximation Using Hilbert Curve Basis Selection ⋮ Surface temperature monitoring in liver procurement via functional variance change-point analysis ⋮ Global debiased DC estimations for biased estimators via pro forma regression

Uses Software

• gss
• mgcv
• assist

Cites Work

• Unnamed Item
• Unnamed Item
• A comparison of GCV and GML for choosing the smoothing parameter in the generalized spline smoothing problem
• Multivariate adaptive regression splines
• Smoothing noisy data with spline functions: Estimating the correct degree of smoothing by the method of generalized cross-validation
• Smoothing Splines
• Efficient computation of smoothing splines via adaptive basis sampling
• Hybrid Adaptive Splines
• Smoothers and theC_p, Generalized Maximum Likelihood, and Extended Exponential Criteria
• Computational Limits of A Distributed Algorithm For Smoothing Spline
• Smoothing Spline Gaussian Regression: More Scalable Computation via Efficient Approximation
• A Correspondence Between Bayesian Estimation on Stochastic Processes and Smoothing by Splines

This page was built for publication: Divide and Recombine Approaches for Fitting Smoothing Spline Models with Large Datasets