Cluster feature selection in high-dimensional linear models

DOI10.1142/S2010326317500150zbMath1384.62237OpenAlexW2765894738MaRDI QIDQ4603581

Bingqing Lin, Qi Hua Wang, Zhen Pang

Publication date: 16 February 2018

Published in: Random Matrices: Theory and Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1142/s2010326317500150

zbMATH Keywords

SISvariable selectionvariable screeningelastic net

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Statistical ranking and selection procedures (62F07)

Related Items (1)

Fast feature selection via streamwise procedure for massive data

Uses Software

• glmnet

Cites Work

• Unnamed Item
• Unnamed Item
• Statistics for high-dimensional data. Methods, theory and applications.
• Robust rank correlation based screening
• High-dimensional variable screening and bias in subsequent inference, with an empirical comparison
• On the adaptive elastic net with a diverging number of parameters
• Forward Regression for Ultra-High Dimensional Variable Screening
• Nonparametric Independence Screening in Sparse Ultra-High-Dimensional Additive Models
• Extended Bayesian information criteria for model selection with large model spaces
• Sure Independence Screening for Ultrahigh Dimensional Feature Space
• High Dimensional Variable Selection via Tilting
• Feature Screening via Distance Correlation Learning
• Regularization and Variable Selection Via the Elastic Net
• High Dimensional Ordinary Least Squares Projection for Screening Variables

This page was built for publication: Cluster feature selection in high-dimensional linear models