Inference for sparse linear regression based on the leave-one-covariate-out solution path
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Publication:6164734
DOI10.1080/03610926.2022.2032171arXiv2005.03694OpenAlexW4210762879MaRDI QIDQ6164734
Xiangyang Cao, Dewei Wang, Karl B. Gregory
Publication date: 28 July 2023
Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2005.03694
Cites Work
- Unnamed Item
- Sure independence screening in generalized linear models with NP-dimensionality
- On asymptotically optimal confidence regions and tests for high-dimensional models
- Nearly unbiased variable selection under minimax concave penalty
- Distribution-Free Predictive Inference For Regression
- The Adaptive Lasso and Its Oracle Properties
- Rates of convergence of the adaptive LASSO estimators to the oracle distribution and higher order refinements by the bootstrap
- Covariate assisted screening and estimation
- High-dimensional variable selection
- High-dimensional inference: confidence intervals, \(p\)-values and R-software \texttt{hdi}
- A significance test for the lasso
- High-dimensional variable screening and bias in subsequent inference, with an empirical comparison
- Perturbation bootstrap in adaptive Lasso
- Pathwise coordinate optimization
- The Dantzig selector: statistical estimation when \(p\) is much larger than \(n\). (With discussions and rejoinder).
- Confidence Intervals and Hypothesis Testing for High-Dimensional Regression
- p-Values for High-Dimensional Regression
- Nonparametric Independence Screening in Sparse Ultra-High-Dimensional Additive Models
- Bootstrapping Lasso Estimators
- On the existence of maximum likelihood estimates in logistic regression models
- Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
- Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data
- Sure Independence Screening for Ultrahigh Dimensional Feature Space
- Confidence Intervals for Low Dimensional Parameters in High Dimensional Linear Models
- Random forests
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