Assessing Tuning Parameter Selection Variability in Penalized Regression

DOI10.1080/00401706.2018.1513380MaRDI QIDQ6621631

Leonard A. Stefanski, Wenhao Hu, Eric B. Laber, Unnamed Author

Publication date: 18 October 2024

Published in: Technometrics (Search for Journal in Brave)

Mathematics Subject Classification ID

Applications of statistics (62Pxx)

Cites Work

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• The Adaptive Lasso and Its Oracle Properties
• Consistent tuning parameter selection in high dimensional sparse linear regression
• Controlling the false discovery rate via knockoffs
• Bootstrapping regression models
• Estimating the dimension of a model
• Shrinkage Tuning Parameter Selection with a Diverging number of Parameters
• Extended Bayesian information criteria for model selection with large model spaces
• Generalized Cross-Validation as a Method for Choosing a Good Ridge Parameter
• Building Multiple Regression Models Interactively
• P Values Maximized Over a Confidence Set for the Nuisance Parameter
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Sure Independence Screening for Ultrahigh Dimensional Feature Space
• Stability Selection
• Variable Selection with Error Control: Another Look at Stability Selection
• Regularization Parameter Selections via Generalized Information Criterion
• Regularization and Variable Selection Via the Elastic Net
• Tuning Parameter Selection for the Adaptive Lasso Using ERIC
• Some Comments on C P
• Tuning Parameter Selection in High Dimensional Penalized Likelihood
• A new look at the statistical model identification

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