Multi-step adaptive elastic-net: reducing false positives in high-dimensional variable selection

DOI10.1080/00949655.2015.1016944OpenAlexW2146986739WikidataQ59112835 ScholiaQ59112835MaRDI QIDQ5220939

Nan Xiao, Qingsong Xu

Publication date: 27 March 2020

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00949655.2015.1016944

zbMATH Keywords

high dimensionalityvariable selectionelastic netadaptive regularizationmulti-step shrinkage

Mathematics Subject Classification ID

Ridge regression; shrinkage estimators (Lasso) (62J07) Linear regression; mixed models (62J05)

Related Items (3)

Penalized \(M\)-estimation based on standard error adjusted adaptive elastic-net ⋮ Optimal observations-based retrieval of topography in 2D shallow water equations using PC-EnKF ⋮ Sparsity-promoting elastic net method with rotations for high-dimensional nonlinear inverse problem

Uses Software

• glmnet

Cites Work

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• The Adaptive Lasso and Its Oracle Properties
• One-step sparse estimates in nonconcave penalized likelihood models
• Least angle regression. (With discussion)
• On the adaptive elastic net with a diverging number of parameters
• Group Variable Selection with Oracle Property by Weight-Fused Adaptive Elastic Net Model for Strongly Correlated Data
• Grouping Variable Selection by Weight Fused Elastic Net for Multi-Collinear Data
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• L 1-Regularization Path Algorithm for Generalized Linear Models
• Group variable selection via SCAD-L₂
• Regularization and Variable Selection Via the Elastic Net
• Ridge Regression: Biased Estimation for Nonorthogonal Problems
• The Mnet method for variable selection

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