Penalized Quantile Regression for Distributed Big Data Using the Slack Variable Representation

DOI10.1080/10618600.2020.1840996OpenAlexW3095759100MaRDI QIDQ5066441

Nan Lin, Ye Fan, Xian-Jun Yin

Publication date: 29 March 2022

Published in: Journal of Computational and Graphical Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/10618600.2020.1840996

zbMATH Keywords

ADMMquantile regressionbig datanonconvex penalty

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (3)

Distributed smoothed rank regression with heterogeneous errors for massive data ⋮ Distributed Censored Quantile Regression ⋮ Communication-efficient distributed estimation for high-dimensional large-scale linear regression

Uses Software

• QICD

Cites Work

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• Global convergence of ADMM in nonconvex nonsmooth optimization
• Distributed inference for quantile regression processes
• Adaptive robust variable selection
• Quantile Regression for Large-Scale Applications
• Regression Quantiles
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Prox-regular functions in variational analysis
• ADMM for Penalized Quantile Regression in Big Data

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