Non-asymptotic error bound for optimal prediction of function-on-function regression by RKHS approach

DOI10.1007/S10114-021-9346-4zbMath1492.60295OpenAlexW4200021138MaRDI QIDQ2131156

Yanyan Li

Publication date: 25 April 2022

Published in: Acta Mathematica Sinica. English Series (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10114-021-9346-4

zbMATH Keywords

integral operatorreproducing kernel Hilbert spaceregularized least squaresfunction-on-function regressionnon-asymptotic error bound

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Interacting random processes; statistical mechanics type models; percolation theory (60K35)

Related Items (1)

Domain Generalization by Functional Regression

Uses Software

• fda (R)

Cites Work

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• Functional linear regression analysis for longitudinal data
• Regression when both response and predictor are functions
• Minimax prediction for functional linear regression with functional responses in reproducing kernel Hilbert spaces
• A reproducing kernel Hilbert space approach to functional linear regression
• Regression models for functional data by reproducing kernel Hilbert spaces methods
• Prediction in functional linear regression
• Methodology and convergence rates for functional linear regression
• Modeling and prediction of children's growth data via functional principal component analysis
• Applied functional data analysis. Methods and case studies
• Nonparametric shape-restricted regression
• Empirical likelihood inference for functional coefficient ARCH-M model
• Analysis of regularized least squares for functional linear regression model
• Support vector machines are universally consistent
• Optimum bounds for the distributions of martingales in Banach spaces
• Learning rates for least square regressions with coefficient regularization
• Nonasymptotic upper bounds for the reconstruction error of PCA
• Penalized function-on-function regression
• Optimal rates for the regularized least-squares algorithm
• Asymptotics of prediction in functional linear regression with functional outputs
• On rates of convergence in functional linear regression
• Functional data analysis.
• Nonparametric functional data analysis. Theory and practice.
• 10.1162/153244302760185252
• An Introduction to the Theory of Reproducing Kernel Hilbert Spaces
• Optimal Penalized Function-on-Function Regression Under a Reproducing Kernel Hilbert Space Framework
• Introduction to Functional Data Analysis
• Minimax and Adaptive Prediction for Functional Linear Regression
• Theoretical Foundations of Functional Data Analysis, with an Introduction to Linear Operators
• Tensor Regression with Applications in Neuroimaging Data Analysis
• Groupwise Registration of Multimodal Images by an Efficient Joint Entropy Minimization Scheme
• Learning Bounds for Kernel Regression Using Effective Data Dimensionality
• Estimation of atmospheric PSF parameters for hyperspectral imaging

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