SCAD‐penalized quantile regression for high‐dimensional data analysis and variable selection
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Publication:6066203
DOI10.1111/stan.12056OpenAlexW1586801766MaRDI QIDQ6066203
Xiaoguang Wang, Unnamed Author, Lixin Song, Muhammad Amin
Publication date: 12 December 2023
Published in: Statistica Neerlandica (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1111/stan.12056
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Quantile regression of ultra-high dimensional partially linear varying-coefficient model with missing observations ⋮ An elastic-net penalized expectile regression with applications
Cites Work
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- Nearly unbiased variable selection under minimax concave penalty
- The Adaptive Lasso and Its Oracle Properties
- Robust rank correlation based screening
- One-step sparse estimates in nonconcave penalized likelihood models
- Lasso-type recovery of sparse representations for high-dimensional data
- Hedonic housing prices and the demand for clean air
- On parameters of increasing dimensions
- Interquantile shrinkage and variable selection in quantile regression
- Limiting distributions for \(L_1\) regression estimators under general conditions
- Robust regression: Asymptotics, conjectures and Monte Carlo
- Asymptotics for Lasso-type estimators.
- Nonconcave penalized likelihood with a diverging number of parameters.
- Quantile regression for longitudinal data
- Asymptotic properties of bridge estimators in sparse high-dimensional regression models
- \(\ell_1\)-penalized quantile regression in high-dimensional sparse models
- Reappraising Medfly Longevity
- Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
- Shrinkage and Penalized Likelihood as Methods to Improve Predictive Accuracy
- Application of Shrinkage Techniques in Logistic Regression Analysis: A Case Study
- Semiparametric Estimation of Regression Quantiles with Application to Standardizing Weight for Height and Age in US Children
- Sure Independence Screening for Ultrahigh Dimensional Feature Space
- Asymptotic oracle properties of SCAD-penalized least squares estimators
- Smoothly Clipped Absolute Deviation on High Dimensions
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