Advanced algorithms for penalized quantile and composite quantile regression

DOI10.1007/s00180-020-01010-1zbMath1505.62320arXiv1709.04126OpenAlexW3041077475MaRDI QIDQ1995843

Linglong Kong, Bei Jiang, Di Niu, Matthew Pietrosanu, Jueyu Gao

Publication date: 25 February 2021

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

Full work available at URL: https://arxiv.org/abs/1709.04126

zbMATH Keywords

interior pointalternating direction method of multipliersadaptive Lassocoordinate descentmajorize minimization

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Nonparametric regression and quantile regression (62G08)

Related Items (7)

Fast quantile regression in reproducing kernel Hilbert space ⋮ Single-index composite quantile regression for ultra-high-dimensional data ⋮ Model-based recursive partitioning algorithm to penalized non-crossing multiple quantile regression for the right-censored data ⋮ Doubly robust weighted composite quantile regression based on SCAD‐L₂ ⋮ Distributed Sparse Composite Quantile Regression in Ultrahigh Dimensions ⋮ A majorization-minimization scheme forL₂support vector regression ⋮ A convex programming solution based debiased estimator for quantile with missing response and high-dimensional covariables

Uses Software

• quantreg
• glmnet
• Rcpp
• RcppArmadillo
• cqrReg

Cites Work

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• ADMM for Penalized Quantile Regression in Big Data

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