High-dimensional grouped folded concave penalized estimation via the LLA algorithm
From MaRDI portal
Publication:1726165
DOI10.1016/j.jkss.2018.08.006zbMath1411.62188OpenAlexW2890919124MaRDI QIDQ1726165
Publication date: 19 February 2019
Published in: Journal of the Korean Statistical Society (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.jkss.2018.08.006
high-dimensional linear modelslocal linear approximationoracle estimatorfolded concave penaltygrouped variable selection
Estimation in multivariate analysis (62H12) Ridge regression; shrinkage estimators (Lasso) (62J07) Linear regression; mixed models (62J05)
Cites Work
- Unnamed Item
- Unnamed Item
- Nearly unbiased variable selection under minimax concave penalty
- The Adaptive Lasso and Its Oracle Properties
- Consistent group selection in high-dimensional linear regression
- One-step sparse estimates in nonconcave penalized likelihood models
- The benefit of group sparsity
- A note on adaptive group Lasso
- Strong oracle optimality of folded concave penalized estimation
- The Concave-Convex Procedure
- Sparse optimization for nonconvex group penalized estimation
- Nonconcave Penalized Likelihood With NP-Dimensionality
- Estimation And Selection Via Absolute Penalized Convex Minimization And Its Multistage Adaptive Applications
- Model Selection and Estimation in Regression with Grouped Variables
- Group descent algorithms for nonconvex penalized linear and logistic regression models with grouped predictors
- A fast unified algorithm for solving group-lasso penalize learning problems
- A selective review of group selection in high-dimensional models
- 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