Distributed Generalized Cross-Validation for Divide-and-Conquer Kernel Ridge Regression and Its Asymptotic Optimality

DOI10.1080/10618600.2019.1586714OpenAlexW2942656440WikidataQ128164490 ScholiaQ128164490MaRDI QIDQ3391209

Ganggang Xu, Guang Cheng, Zuofeng Shang

Publication date: 28 March 2022

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

Full work available at URL: https://arxiv.org/abs/1612.05907

zbMATH Keywords

divide-and-conquerkernel ridge regressionoptimal tuningdistributed GCV

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (7)

Smoothing Splines Approximation Using Hilbert Curve Basis Selection ⋮ Distributed estimation with empirical likelihood ⋮ Global debiased DC estimations for biased estimators via pro forma regression ⋮ Nonparametric inference on smoothed quantile regression process ⋮ Unbalanced distributed estimation and inference for the precision matrix in Gaussian graphical models ⋮ Distributed Censored Quantile Regression ⋮ Distributed adaptive nearest neighbor classifier: algorithm and theory

Uses Software

• R
• gss

Cites Work

• A partially linear framework for massive heterogeneous data
• Asymptotic optimality and efficient computation of the leave-subject-out cross-validation
• Smoothing splines: Regression, derivatives and deconvolution
• GACV for quantile smoothing splines
• Asymptotic optimality of \(C_ L\) and generalized cross-validation in ridge regression with application to spline smoothing
• Smoothing noisy data with spline functions: Estimating the correct degree of smoothing by the method of generalized cross-validation
• Optimal smoothing in nonparametric mixed-effect models
• Divide and Conquer Kernel Ridge Regression: A Distributed Algorithm with Minimax Optimal Rates
• A split-and-conquer approach for analysis of
• Kernel techniques: From machine learning to meshless methods
• Computational Limits of A Distributed Algorithm For Smoothing Spline
• Stable and Efficient Multiple Smoothing Parameter Estimation for Generalized Additive Models
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