Nonparametric distributed learning under general designs

DOI10.1214/20-EJS1733zbMath1466.62364MaRDI QIDQ2199703

Meimei Liu, Guang Cheng, Zuofeng Shang

Publication date: 14 September 2020

Published in: Electronic Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.ejs/1597975224

zbMATH Keywords

nonparametric testingminimax optimalitykernel ridge regressiondivide and conquercomputational limit

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Nonparametric hypothesis testing (62G10) Ridge regression; shrinkage estimators (Lasso) (62J07) Minimax procedures in statistical decision theory (62C20) General nonlinear regression (62J02) Response surface designs (62K20)

Related Items (1)

Smoothing Splines Approximation Using Hilbert Curve Basis Selection

Uses Software

• gss

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Minimax optimal rates of estimation in high dimensional additive models
• Maximum penalized likelihood estimation. Volume II: Regression
• High-dimensional additive modeling
• A central limit theorem for generalized quadratic forms
• Additive regression and other nonparametric models
• Asymptotically minimax hypothesis testing for nonparametric alternatives. I
• Bayesian aggregation of average data: an application in drug development
• Generalized likelihood ratio statistics and Wilks phenomenon
• The covering number in learning theory
• Randomized sketches for kernels: fast and optimal nonparametric regression
• Distributed statistical estimation and rates of convergence in normal approximation
• Local and global asymptotic inference in smoothing spline models
• Local Rademacher complexities
• On the mathematical foundations of learning
• Divide and Conquer Kernel Ridge Regression: A Distributed Algorithm with Minimax Optimal Rates
• Mathematical Foundations of Infinite-Dimensional Statistical Models
• Computational Limits of A Distributed Algorithm For Smoothing Spline
• The Local Geometry of Testing in Ellipses: Tight Control via Localized Kolmogorov Widths
• An asymptotic analysis of distributed nonparametric methods
• Nonparametric Bayesian Aggregation for Massive Data
• Mercer’s Theorem, Feature Maps, and Smoothing
• Minimax-optimal rates for sparse additive models over kernel classes via convex programming
• Understanding Gaussian Process Regression Using the Equivalent Kernel
• Learning Bounds for Kernel Regression Using Effective Data Dimensionality

This page was built for publication: Nonparametric distributed learning under general designs