Variable selection in additive quantile regression using nonconcave penalty

DOI10.1080/02331888.2016.1221954zbMath1357.62184OpenAlexW2510923546MaRDI QIDQ2953973

Heng Lian, Kaifeng Zhao

Publication date: 11 January 2017

Published in: Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02331888.2016.1221954

zbMATH Keywords

additive modelsoracle propertySCAD penaltySchwartz-type information criterion

Mathematics Subject Classification ID

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

Cites Work

• Single-index quantile regression
• The Adaptive Lasso and Its Oracle Properties
• Sparsity in multiple kernel learning
• Additive models for quantile regression: model selection and confidence bands
• Conditional growth charts. (With discussion and rejoinder)
• Variable selection in nonparametric additive models
• Quantile regression with varying coefficients
• SCAD-penalized regression in high-dimensional partially linear models
• Quantile regression in partially linear varying coefficient models
• Bivariate tensor-product \(B\)-splines in a partly linear model
• Variable selection and estimation in high-dimensional varying-coefficient models
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• On Additive Conditional Quantiles With High-Dimensional Covariates
• Quantile Regression for Analyzing Heterogeneity in Ultra-High Dimension
• Shrinkage Estimation of the Varying Coefficient Model
• Variable Selection in Nonparametric Varying-Coefficient Models for Analysis of Repeated Measurements
• Nonparametric Estimation of an Additive Quantile Regression Model
• A practical guide to splines.

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