Discussion of ``Correlated variables in regression: clustering and sparse estimation

DOI10.1016/j.jspi.2013.05.020zbMath1432.62200OpenAlexW2008707499MaRDI QIDQ394081

Jacob Bien, Marten H. Wegkamp

Publication date: 24 January 2014

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jspi.2013.05.020

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Ridge regression; shrinkage estimators (Lasso) (62J07) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Linear regression; mixed models (62J05) Measures of association (correlation, canonical correlation, etc.) (62H20)

Uses Software

• glasso

Cites Work

• Unnamed Item
• A well-conditioned estimator for large-dimensional covariance matrices
• Statistics for high-dimensional data. Methods, theory and applications.
• Support vector machines with a reject option
• Honest variable selection in linear and logistic regression models via \(\ell _{1}\) and \(\ell _{1}+\ell _{2}\) penalization
• Standardization and the Group Lasso Penalty
• Regularization and Variable Selection Via the Elastic Net
• Model Selection and Estimation in Regression with Grouped Variables

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