Robust variable selection based on the random quantile LASSO

DOI10.1080/03610918.2019.1643886OpenAlexW2964244086MaRDI QIDQ5086334

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Publication date: 5 July 2022

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610918.2019.1643886

zbMATH Keywords

robustnessquantile regressionvariable selectionrandom LASSO

Mathematics Subject Classification ID

Statistics (62-XX)

Uses Software

• glmnet

Cites Work

• The Adaptive Lasso and Its Oracle Properties
• The least trimmed quantile regression
• Random lasso
• The catline for deep regression
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• \(\ell_1\)-penalized quantile regression in high-dimensional sparse models
• Adaptive robust variable selection
• Extended BIC for small-n-large-P sparse GLM
• Weighted Wilcoxon‐Type Smoothly Clipped Absolute Deviation Method
• Regression Quantiles
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Penalized Composite Quasi-Likelihood for Ultrahigh Dimensional Variable Selection
• Fully efficient robust estimation, outlier detection, and variable selection via penalized regression
• Variable selection for censored quantile regression
• Quantile Regression for Analyzing Heterogeneity in Ultra-High Dimension
• A Scalable Empirical Bayes Approach to Variable Selection in Generalized Linear Models
• Robust estimation in partially linear regression models
• Uniform post-selection inference for least absolute deviation regression and other Z-estimation problems
• Regularization and Variable Selection Via the Elastic Net
• Robust Variable Selection With Exponential Squared Loss
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