The group exponential lasso for bi-level variable selection

DOI10.1111/biom.12300zbMath1419.62316OpenAlexW1589848011WikidataQ41216191 ScholiaQ41216191MaRDI QIDQ123390

Patrick Breheny, Patrick Breheny

Publication date: 13 March 2015

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/biom.12300

zbMATH Keywords

rare variantspenalized regressiongroup variable selection

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Ridge regression; shrinkage estimators (Lasso) (62J07) Applications of statistics to biology and medical sciences; meta analysis (62P10)

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Uses Software

• glmnet

Cites Work

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• Coordinate descent algorithms for nonconvex penalized regression, with applications to biological feature selection
• Penalized methods for bi-level variable selection
• Nearly unbiased variable selection under minimax concave penalty
• The Adaptive Lasso and Its Oracle Properties
• Coordinate descent algorithms for lasso penalized regression
• A group bridge approach for variable selection
• Ideal spatial adaptation by wavelet shrinkage
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Model Selection and Estimation in Regression with Grouped Variables
• A selective review of group selection in high-dimensional models

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