Model selection and estimation in high dimensional regression models with group SCAD

DOI10.1016/j.spl.2015.04.017zbMath1328.62241OpenAlexW2158699631MaRDI QIDQ893964

Yao Wang, Xiao Guo, Jiang-Lun Wu, Hai Zhang

Publication date: 23 November 2015

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://cronfa.swan.ac.uk/Record/cronfa22316/Download/0022316-22022017111611.pdf

zbMATH Keywords

sparsitygroup selectionoracle propertyhigh dimensiongroup SCAD

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Asymptotic properties of nonparametric inference (62G20)

Related Items

A high-dimensional M-estimator framework for bi-level variable selection, Adaptive group Lasso selection in quantile models, Local optimality for stationary points of group zero-norm regularized problems and equivalent surrogates, Detection of similar successive groups in a model with diverging number of variable groups, Envelope-based sparse reduced-rank regression for multivariate linear model, High-dimensional sparse portfolio selection with nonnegative constraint

Cites Work

• Unnamed Item
• Unnamed Item
• 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
• A note on adaptive group Lasso
• Estimating the dimension of a model
• Nonconcave penalized likelihood with a diverging number of parameters.
• Strong oracle optimality of folded concave penalized estimation
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Smoothly Clipped Absolute Deviation on High Dimensions
• Model Selection and Estimation in Regression with Grouped Variables
• Some Comments on C P
• Group descent algorithms for nonconvex penalized linear and logistic regression models with grouped predictors
• A selective review of group selection in high-dimensional models