A high-dimensional M-estimator framework for bi-level variable selection

DOI10.1007/s10463-021-00809-zOpenAlexW2991365059MaRDI QIDQ2135521

Bin Luo, Xiaoli Gao

Publication date: 9 May 2022

Published in: Annals of the Institute of Statistical Mathematics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1911.11646

zbMATH Keywords

high dimensionalityM-estimationnon-convexitybi-level variable selectionestimation consistency

Mathematics Subject Classification ID

Statistics (62-XX)

Uses Software

• glasso

Cites Work

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• Penalized methods for bi-level variable selection
• The group exponential lasso for bi-level variable selection
• Nearly unbiased variable selection under minimax concave penalty
• Gradient methods for minimizing composite functions
• Consistent group selection in high-dimensional linear regression
• Model selection and estimation in high dimensional regression models with group SCAD
• Redescending \(M\)-estimators
• Statistical consistency and asymptotic normality for high-dimensional robust \(M\)-estimators
• Robust group non-convex estimations for high-dimensional partially linear models
• A group bridge approach for variable selection
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Model Selection and Estimation in Regression with Grouped Variables
• Regularized M-estimators with nonconvexity: Statistical and algorithmic theory for local optima
• A selective review of group selection in high-dimensional models

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