Analysis of the rate of convergence of two regression estimates defined by neural features which are easy to implement
From MaRDI portal
Publication:6200889
DOI10.1214/23-ejs2207OpenAlexW4391777660MaRDI QIDQ6200889
Jeongik Cho, Alina Braun, Adam Krzyżak, Michael Kohler
Publication date: 25 March 2024
Published in: Electronic Journal of Statistics (Search for Journal in Brave)
Full work available at URL: https://projecteuclid.org/journals/electronic-journal-of-statistics/volume-18/issue-1/Analysis-of-the-rate-of-convergence-of-two-regression-estimates/10.1214/23-EJS2207.full
Nonparametric regression and quantile regression (62G08) Neural nets and related approaches to inference from stochastic processes (62M45)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- On projection pursuit regression
- Projection pursuit
- Additive regression and other nonparametric models
- On a conjecture of Huber concerning the convergence of projection pursuit regression
- Distribution-free consistency results in nonparametric discrimination and regression function estimation
- A simple lemma on greedy approximation in Hilbert space and convergence rates for projection pursuit regression and neural network training
- Approximation and estimation bounds for artificial neural networks
- The use of polynomial splines and their tensor products in multivariate function estimation. (With discussion)
- On estimation of surrogate models for multivariate computer experiments
- Optimal global rates of convergence for nonparametric regression
- A distribution-free theory of nonparametric regression
- On the rate of convergence of fully connected deep neural network regression estimates
- Nonparametric regression using deep neural networks with ReLU activation function
- Error bounds for approximations with deep ReLU networks
- On deep learning as a remedy for the curse of dimensionality in nonparametric regression
- Optimal smoothing in single-index models
- Adaptive estimation under single-index constraint in a regression model
- Rate-optimal estimation for a general class of nonparametric regression models with unknown link functions
- Investigating Smooth Multiple Regression by the Method of Average Derivatives
- Variable selection for the single-index model
- Universal approximation bounds for superpositions of a sigmoidal function
- Penalized Spline Estimation for Partially Linear Single-Index Models
- Neural Network Learning
- Estimation of a Regression Function by Maxima of Minima of Linear Functions
- Estimation of a Function of Low Local Dimensionality by Deep Neural Networks
- Deep Network Approximation for Smooth Functions
- Nonparametric Regression Based on Hierarchical Interaction Models
- Deep Neural Networks with ReLU-Sine-Exponential Activations Break Curse of Dimensionality in Approximation on Hölder Class
