Consistency of Ridge Function Fields for Varying Nonparametric Regression
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Publication:5321904
DOI10.1080/03610920802395702zbMath1167.62033OpenAlexW2028675346MaRDI QIDQ5321904
Robert Frouin, Bruno Pelletier
Publication date: 16 July 2009
Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/03610920802395702
least squaresnonparametric regressionuniversal consistencyvarying coefficients modelridge function approximation
Nonparametric regression and quantile regression (62G08) Ridge regression; shrinkage estimators (Lasso) (62J07) Nonparametric estimation (62G05)
Cites Work
- Varying-coefficient single-index model
- On best approximation by ridge functions
- Fundamentality of ridge functions
- Statistical estimation in varying coefficient models
- Multilayer feedforward networks are universal approximators
- A distribution-free theory of nonparametric regression
- Random rates in anisotropic regression. (With discussion)
- Approximation by ridge function fields over compact sets
- Adaptive regression estimation with multilayer feedforward neural networks
- Universal approximation bounds for superpositions of a sigmoidal function
- Adaptive Varying-Coefficient Linear Models
- On the Uniform Convergence of Relative Frequencies of Events to Their Probabilities
- Convergence of stochastic processes
- Approximation by superpositions of a sigmoidal function
- Error bounds for approximation with neural networks
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