Exploiting Disagreement Between High-Dimensional Variable Selectors for Uncertainty Visualization
From MaRDI portal
Publication:5084434
DOI10.1080/10618600.2021.2000421OpenAlexW3211911790MaRDI QIDQ5084434
Piotr Fryzlewicz, Christine Yuen
Publication date: 24 June 2022
Published in: Journal of Computational and Graphical Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2003.02791
Related Items
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- On asymptotically optimal confidence regions and tests for high-dimensional models
- Nearly unbiased variable selection under minimax concave penalty
- The Adaptive Lasso and Its Oracle Properties
- Exact post-selection inference, with application to the Lasso
- Asymptotic properties of Lasso+mLS and Lasso+Ridge in sparse high-dimensional linear regression
- Cross-validation for selecting a model selection procedure
- Relaxed Lasso
- Hedonic housing prices and the demand for clean air
- Uniform asymptotic inference and the bootstrap after model selection
- Debiasing the Lasso: optimal sample size for Gaussian designs
- The Dantzig selector: statistical estimation when \(p\) is much larger than \(n\). (With discussions and rejoinder).
- Bootstrapping Lasso Estimators
- Extended Bayesian information criteria for model selection with large model spaces
- A Selective Overview of Variable Selection in High Dimensional Feature Space (Invited Review Article)
- Adaptive Regression by Mixing
- Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
- Stability Selection
- Variable Selection with Error Control: Another Look at Stability Selection
- Ranking-Based Variable Selection for high-dimensional data
- Regularization and Variable Selection Via the Elastic Net
- Toward an objective and reproducible model choice via variable selection deviation
- Decoupling Shrinkage and Selection in Bayesian Linear Models: A Posterior Summary Perspective
- Model Selection and Estimation in Regression with Grouped Variables
- Confidence Intervals for Low Dimensional Parameters in High Dimensional Linear Models
- Combining Linear Regression Models