Sampling Lasso quantile regression for large-scale data

DOI10.1080/03610918.2016.1277750zbMath1392.62220OpenAlexW2575755675MaRDI QIDQ4563390

Qifa Xu, Xue Huang, Fang Sun, Chao Cai, Cuixia Jiang

Publication date: 1 June 2018

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610918.2016.1277750

zbMATH Keywords

samplingquantile regressionLassolarge-scale dataSLQR

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Ridge regression; shrinkage estimators (Lasso) (62J07)

Related Items (2)

Automatic variable selection in a linear model on massive data ⋮ An elastic-net penalized expectile regression with applications

Uses Software

• CAViaR

Cites Work

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• The Adaptive Lasso and Its Oracle Properties
• Additive models for quantile regression: model selection and confidence bands
• Composite quantile regression and the oracle model selection theory
• GACV for quantile smoothing splines
• Regularized quantile regression under heterogeneous sparsity with application to quantitative genetic traits
• Transformed linear quantile regression with censored survival data
• Quantile regression for longitudinal data
• An exponentially weighted quantile regression via SVM with application to estimating multiperiod VaR
• Selecting rows and columns for training support vector regression models with large retail datasets
• \(\ell_1\)-penalized quantile regression in high-dimensional sparse models
• The Fast Cauchy Transform and Faster Robust Linear Regression
• Penalized LAD Regression for Single-index Models
• Bayesian Elastic Net Tobit Quantile Regression
• Quantile Regression for Large-Scale Applications
• Sampling algorithms for l₂ regression and applications
• Regression Quantiles
• Quantile smoothing splines
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Quantile regression and variable selection for the single-index model
• An IV Model of Quantile Treatment Effects
• Low-distortion subspace embeddings in input-sparsity time and applications to robust linear regression
• Randomized maximum-contrast selection: subagging for large-scale regression

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