Assessing Tuning Parameter Selection Variability in Penalized Regression
From MaRDI portal
Publication:6621631
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)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- 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
This page was built for publication: Assessing Tuning Parameter Selection Variability in Penalized Regression