A random-effect model approach for group variable selection

DOI10.1016/j.csda.2015.02.020zbMath1468.62115OpenAlexW2147780093MaRDI QIDQ1663264

Youngjo Lee, Sangin Lee, Yudi Pawitan

Publication date: 21 August 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2015.02.020

zbMATH Keywords

model selectionLassosparse regressionhierarchical likelihood

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Ridge regression; shrinkage estimators (Lasso) (62J07) Linear regression; mixed models (62J05)

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Cites Work

• Unnamed Item
• Penalized methods for bi-level variable selection
• Nearly unbiased variable selection under minimax concave penalty
• The Adaptive Lasso and Its Oracle Properties
• Group coordinate descent algorithms for nonconvex penalized regression
• A note on adaptive group Lasso
• Asymptotic properties of bridge estimators in sparse high-dimensional regression models
• Variable selection using MM algorithms
• A new sparse variable selection via random-effect model
• A group bridge approach for variable selection
• The Concave-Convex Procedure
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Model Selection and Estimation in Regression with Grouped Variables
• Double Hierarchical Generalized Linear Models (With Discussion)
• A selective review of group selection in high-dimensional models

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