Adaptive elastic net-penalized quantile regression for variable selection
From MaRDI portal
Publication:5077881
DOI10.1080/03610926.2018.1508711OpenAlexW2913139111MaRDI QIDQ5077881
Publication date: 20 May 2022
Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/03610926.2018.1508711
quantile regressionvariable selectionadaptive elastic nethigh-dimensional linear regressionweak oracle property
Related Items (2)
Elastic net penalized quantile regression model ⋮ Adaptive elastic-net selection in a quantile model with diverging number of variable groups
Cites Work
- Unnamed Item
- Unnamed Item
- A unified approach to model selection and sparse recovery using regularized least squares
- The Adaptive Lasso and Its Oracle Properties
- The \(L_1\) penalized LAD estimator for high dimensional linear regression
- Lasso-type recovery of sparse representations for high-dimensional data
- A coordinate descent algorithm for computing penalized smooth quantile regression
- A note on quantile feature screening via distance correlation
- Simultaneous analysis of Lasso and Dantzig selector
- \(\ell_1\)-penalized quantile regression in high-dimensional sparse models
- Adaptive robust variable selection
- The Dantzig selector: statistical estimation when \(p\) is much larger than \(n\). (With discussions and rejoinder).
- Regression Quantiles
- Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
- Penalized Composite Quasi-Likelihood for Ultrahigh Dimensional Variable Selection
- Feature Screening via Distance Correlation Learning
- Quantile Regression for Analyzing Heterogeneity in Ultra-High Dimension
- Conditional quantile screening in ultrahigh-dimensional heterogeneous data
- Regularization and Variable Selection Via the Elastic Net
This page was built for publication: Adaptive elastic net-penalized quantile regression for variable selection
