An effective method to reduce the computational complexity of composite quantile regression

DOI10.1007/s00180-017-0749-8zbMath1417.62100OpenAlexW2739362286MaRDI QIDQ1695421

Yanke Wu, Mao-Zai Tian

Publication date: 7 February 2018

Published in: Computational Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00180-017-0749-8

zbMATH Keywords

computational complexitydual problemlinear programmingquantile regressioncomposite quantile regression

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Nonparametric regression and quantile regression (62G08) Asymptotic properties of nonparametric inference (62G20) Linear regression; mixed models (62J05) Complexity and performance of numerical algorithms (65Y20)

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• The Adaptive Lasso and Its Oracle Properties
• A note on L-estimates for linear models
• Hierarchical linear regression models for conditional quantiles
• Composite quantile regression and the oracle model selection theory
• Bayesian Quantile Regression for Censored Data
• Linear Quantile Regression Based on EM Algorithm
• Regression Quantiles
• Quantile smoothing splines
• Bayesian quantile inference
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Local Composite Quantile Regression Smoothing: An Efficient and Safe Alternative to Local Polynomial Regression
• Gibbs sampling methods for Bayesian quantile regression
• Bayesian quantile regression

This page was built for publication: An effective method to reduce the computational complexity of composite quantile regression