SCAD penalized rank regression with a diverging number of parameters

DOI10.1016/j.jmva.2014.09.014zbMath1302.62130OpenAlexW1999681893MaRDI QIDQ476249

Jing Lv, Chaohui Guo, Hu Yang

Publication date: 28 November 2014

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jmva.2014.09.014

zbMATH Keywords

model selectionrank regressionhigh dimensionalityrobust estimationoracle propertySCAD

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Asymptotic distribution theory in statistics (62E20)

Related Items (6)

Rank method for partial functional linear regression models ⋮ On the oracle property of a generalized adaptive elastic-net for multivariate linear regression with a diverging number of parameters ⋮ Recognition and variable selection in sparse spatial panel data models with fixed effects ⋮ A semi-parametric approach to feature selection in high-dimensional linear regression models ⋮ Penalized profile least squares-based statistical inference for varying coefficient partially linear errors-in-variables models ⋮ Efficient and doubly-robust methods for variable selection and parameter estimation in longitudinal data analysis

Uses Software

• wwcode

Cites Work

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• Model-Free Feature Screening for Ultrahigh-Dimensional Data
• Least squares approximation with a diverging number of parameters
• Composite quantile regression and the oracle model selection theory
• One-step sparse estimates in nonconcave penalized likelihood models
• The sparsity and bias of the LASSO selection in high-dimensional linear regression
• Limiting distributions for \(L_1\) regression estimators under general conditions
• Nonconcave penalized likelihood with a diverging number of parameters.
• On the adaptive elastic net with a diverging number of parameters
• \(\ell_1\)-penalized quantile regression in high-dimensional sparse models
• Coordinate descent algorithms for lasso penalized regression
• Wilcoxon-type generalized Bayesian information criterion
• Weighted Wilcoxon‐Type Smoothly Clipped Absolute Deviation Method
• Estimating Regression Coefficients by Minimizing the Dispersion of the Residuals
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Sure Independence Screening for Ultrahigh Dimensional Feature Space
• Feature Screening via Distance Correlation Learning
• Asymptotic oracle properties of SCAD-penalized least squares estimators
• Tuning parameter selectors for the smoothly clipped absolute deviation method

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