Variable selection via composite quantile regression with dependent errors

DOI10.1111/stan.12035MaRDI QIDQ6066191

Xin-Yuan Song, Zhong-yi Zhu, Yanlin Tang

Publication date: 12 December 2023

Published in: Statistica Neerlandica (Search for Journal in Brave)

zbMATH Keywords

quantile regressionuniversal lower boundshort-range dependenceadaptive Lassostrictly stationary

Mathematics Subject Classification ID

Linear inference, regression (62Jxx) Parametric inference (62Fxx) Nonparametric inference (62Gxx)

Related Items (2)

Test by adaptive Lasso quantile method for real-time detection of a change-point ⋮ Composite quantile estimation in partial functional linear regression model based on polynomial spline

Cites Work

• The Adaptive Lasso and Its Oracle Properties
• A note on L-estimates for linear models
• Composite quantile regression and the oracle model selection theory
• \(M\)-estimation of linear models with dependent errors
• Estimating the dimension of a model
• Limiting distributions for \(L_1\) regression estimators under general conditions
• New efficient estimation and variable selection methods for semiparametric varying-coefficient partially linear models
• M-estimation for linear models with spatially-correlated errors
• A unified variable selection approach for varying coefficient models
• EFFICIENT REGRESSIONS VIA OPTIMALLY COMBINING QUANTILE INFORMATION
• Conditional Quantile Estimation for Generalized Autoregressive Conditional Heteroscedasticity Models
• Regression Quantiles
• Penalized Composite Quasi-Likelihood for Ultrahigh Dimensional Variable Selection
• Regression coefficient and autoregressive order shrinkage and selection via the lasso
• Weighted composite quantile regression estimation of DTARCH models
• Robust modelling of DTARCH models
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item

This page was built for publication: Variable selection via composite quantile regression with dependent errors