Regularized quantile regression under heterogeneous sparsity with application to quantitative genetic traits

DOI10.1016/j.csda.2015.10.007zbMath1468.62076arXiv1511.00730OpenAlexW1912364885WikidataQ38992919 ScholiaQ38992919MaRDI QIDQ1659500

Qianchuan He, Eric Holland, Yanhua Wang, Sijian Wang, Linglong Kong, Timothy A. Chan

Publication date: 15 August 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1511.00730

zbMATH Keywords

quantile regressionvariable selectionquantitative traitsgenomic featuresheterogeneous sparsity

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Nonparametric regression and quantile regression (62G08) Ridge regression; shrinkage estimators (Lasso) (62J07)

Related Items (8)

A model-free variable selection method for reducing the number of redundant variables ⋮ Sampling Lasso quantile regression for large-scale data ⋮ Time-varying quantile single-index model for multivariate responses ⋮ Doubly robust weighted composite quantile regression based on SCAD‐L₂ ⋮ Detection of similar successive groups in a model with diverging number of variable groups ⋮ Advanced algorithms for penalized quantile and composite quantile regression ⋮ A convex programming solution based debiased estimator for quantile with missing response and high-dimensional covariables ⋮ Sparse high-dimensional semi-nonparametric quantile regression in a reproducing kernel Hilbert space

Uses Software

• quantreg
• sparsenet

Cites Work

• Unnamed Item
• Unnamed Item
• The Adaptive Lasso and Its Oracle Properties
• Shrinkage estimation of varying covariate effects based on quantile regression
• Regularized simultaneous model selection in multiple quantiles regression
• Interquantile shrinkage and variable selection in quantile regression
• Quantile regression for longitudinal data
• EFFICIENT REGRESSIONS VIA OPTIMALLY COMBINING QUANTILE INFORMATION
• SparseNet: Coordinate Descent With Nonconvex Penalties
• Hierarchically penalized Cox regression with grouped variables
• A group bridge approach for variable selection
• Regression Quantiles
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Recovering Sparse Signals With a Certain Family of Nonconvex Penalties and DC Programming
• Sparsity and Smoothness Via the Fused Lasso
• Quantile Regression for Analyzing Heterogeneity in Ultra-High Dimension

This page was built for publication: Regularized quantile regression under heterogeneous sparsity with application to quantitative genetic traits