Robust High-Dimensional Regression with Coefficient Thresholding and Its Application to Imaging Data Analysis
From MaRDI portal
Publication:6154026
DOI10.1080/01621459.2022.2142590arXiv2109.14856OpenAlexW3203908636MaRDI QIDQ6154026
No author found.
Publication date: 19 March 2024
Published in: Journal of the American Statistical Association (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2109.14856
Cites Work
- Unnamed Item
- Unnamed Item
- Nearly unbiased variable selection under minimax concave penalty
- The Adaptive Lasso and Its Oracle Properties
- Optimal rates of convergence for noisy sparse phase retrieval via thresholded Wirtinger flow
- Gradient methods for minimizing composite functions
- Support recovery without incoherence: a case for nonconvex regularization
- Spatial Bayesian variable selection and grouping for high-dimensional scalar-on-image regression
- The statistical analysis of fMRI data
- One-step sparse estimates in nonconcave penalized likelihood models
- Risk bounds for model selection via penalization
- I-LAMM for sparse learning: simultaneous control of algorithmic complexity and statistical error
- The landscape of empirical risk for nonconvex losses
- Statistical consistency and asymptotic normality for high-dimensional robust \(M\)-estimators
- Strong oracle optimality of folded concave penalized estimation
- Asymptotic Equivalence of Regularization Methods in Thresholded Parameter Space
- On robust regression with high-dimensional predictors
- Phase Retrieval via Wirtinger Flow: Theory and Algorithms
- Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
- Hard thresholding regression
- Sharp Thresholds for High-Dimensional and Noisy Sparsity Recovery Using $\ell _{1}$-Constrained Quadratic Programming (Lasso)
- The Vanishing Gradient Problem During Learning Recurrent Neural Nets and Problem Solutions
- Scalar-on-image regression via the soft-thresholded Gaussian process
- Restricted strong convexity and weighted matrix completion: Optimal bounds with noise
- Model Selection and Estimation in Regression with Grouped Variables
- Robust Estimation of a Location Parameter
- Mini-batch stochastic approximation methods for nonconvex stochastic composite optimization
- A unified framework for high-dimensional analysis of \(M\)-estimators with decomposable regularizers
- A general theory of concave regularization for high-dimensional sparse estimation problems
This page was built for publication: Robust High-Dimensional Regression with Coefficient Thresholding and Its Application to Imaging Data Analysis
