Factor Augmented Sparse Throughput Deep ReLU Neural Networks for High Dimensional Regression
From MaRDI portal
Publication:6651371
DOI10.1080/01621459.2023.2271605MaRDI QIDQ6651371
Publication date: 10 December 2024
Published in: Journal of the American Statistical Association (Search for Journal in Brave)
Related Items (1)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Factor-Adjusted Regularized Model Selection
- Additive regression and other nonparametric models
- The use of polynomial splines and their tensor products in multivariate function estimation. (With discussion)
- Optimal global rates of convergence for nonparametric regression
- A distribution-free theory of nonparametric regression
- A selective overview of deep learning
- On the rate of convergence of fully connected deep neural network regression estimates
- Nonlinear approximation via compositions
- Rejoinder: ``Nonparametric regression using deep neural networks with ReLU activation function
- Error bounds for approximations with deep ReLU networks
- A survey on semi-supervised learning
- On deep learning as a remedy for the curse of dimensionality in nonparametric regression
- Investigating Smooth Multiple Regression by the Method of Average Derivatives
- Universal approximation bounds for superpositions of a sigmoidal function
- Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
- High-Dimensional Statistics
- Deep Neural Networks for Estimation and Inference
- Nonconvex Sparse Regularization for Deep Neural Networks and Its Optimality
- Deep Network Approximation for Smooth Functions
- Nonparametric Regression Based on Hierarchical Interaction Models
- Learning Latent Factors From Diversified Projections and Its Applications to Over-Estimated and Weak Factors
- Introduction to nonparametric estimation
- Approximation by superpositions of a sigmoidal function
This page was built for publication: Factor Augmented Sparse Throughput Deep ReLU Neural Networks for High Dimensional Regression