On the oracle property of a generalized adaptive elastic-net for multivariate linear regression with a diverging number of parameters

DOI10.1016/j.jmva.2017.08.005zbMath1381.62108OpenAlexW2756471043MaRDI QIDQ1679561

Liangyuan Liu, Xin Xin, Jian-Hua Hu

Publication date: 9 November 2017

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

Full work available at URL: https://doi.org/10.1016/j.jmva.2017.08.005

zbMATH Keywords

oracle propertygroup variable selectionmultivariate linear regressiongeneralized adaptive elastic-net

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Linear regression; mixed models (62J05)

Related Items

Variable selection and collinearity processing for multivariate data via row-elastic-net regularization ⋮ A fast and consistent variable selection method for high-dimensional multivariate linear regression with a large number of explanatory variables ⋮ Ridge parameters optimization based on minimizing model selection criterion in multivariate generalized ridge regression ⋮ \(\ell_{2,0}\)-norm based selection and estimation for multivariate generalized linear models ⋮ Adaptive elastic-net selection in a quantile model with diverging number of variable groups

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• The Adaptive Lasso and Its Oracle Properties
• Model determination and estimation for the growth curve model via group SCAD penalty
• SCAD penalized rank regression with a diverging number of parameters
• Profile-kernel likelihood inference with diverging number of parameters
• Regularized multivariate regression for identifying master predictors with application to integrative genomics study of breast cancer
• Asymptotic normality and consistency of a two-stage generalized least squares estimator in the growth curve model
• Input selection and shrinkage in multiresponse linear regression
• SCAD-penalized regression in high-dimensional partially linear models
• Nonconcave penalized likelihood with a diverging number of parameters.
• Estimation and model selection in generalized additive partial linear models for correlated data with diverging number of covariates
• Support union recovery in high-dimensional multivariate regression
• Sparse reduced-rank regression with covariance estimation
• Sure Independence Screening for Ultrahigh Dimensional Feature Space
• Sparse Partial Least Squares Regression for Simultaneous Dimension Reduction and Variable Selection
• Feature Screening via Distance Correlation Learning
• Sparse Reduced-Rank Regression for Simultaneous Dimension Reduction and Variable Selection
• Reduced rank ridge regression and its kernel extensions
• Dimension Reduction and Coefficient Estimation in Multivariate Linear Regression
• Regularization and Variable Selection Via the Elastic Net
• Reduced Rank Stochastic Regression with a Sparse Singular value Decomposition
• Semilinear High-Dimensional Model for Normalization of Microarray Data