Variable selection in high-dimensional partly linear additive models

DOI10.1080/10485252.2012.701300zbMath1254.62053OpenAlexW2027827909MaRDI QIDQ3145401

Heng Lian

Publication date: 20 December 2012

Published in: Journal of Nonparametric Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/10485252.2012.701300

zbMATH Keywords

polynomial splinesBICoracle propertyadaptive lasso

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Numerical computation using splines (65D07) Asymptotic properties of nonparametric inference (62G20) Monte Carlo methods (65C05)

Related Items (6)

A sure independence screening procedure for ultra-high dimensional partially linear additive models ⋮ Smoothing combined generalized estimating equations in quantile partially linear additive models with longitudinal data ⋮ Identification for partially linear regression model with autoregressive errors ⋮ Profile statistical inference for partially linear additive models with a diverging number of parameters ⋮ A robust spline approach in partially linear additive models ⋮ Sparse high-dimensional varying coefficient model: nonasymptotic minimax study

Cites Work

• Unnamed Item
• The Adaptive Lasso and Its Oracle Properties
• Estimation and variable selection for generalized additive partial linear models
• Risk prediction for prostate cancer recurrence through regularized estimation with simultaneous adjustment for nonlinear clinical effects
• The sparsity and bias of the LASSO selection in high-dimensional linear regression
• Variable selection in nonparametric additive models
• Lasso-type recovery of sparse representations for high-dimensional data
• SCAD-penalized regression in high-dimensional partially linear models
• Simultaneous analysis of Lasso and Dantzig selector
• \(\ell_1\)-penalized quantile regression in high-dimensional sparse models
• Variable selection in semiparametric regression modeling
• Shrinkage Tuning Parameter Selection with a Diverging number of Parameters
• Additive partial linear models with measurement errors
• Extended Bayesian information criteria for model selection with large model spaces
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Shrinkage Estimation of the Varying Coefficient Model
• Variable Selection in Nonparametric Varying-Coefficient Models for Analysis of Repeated Measurements
• Model Selection and Estimation in Regression with Grouped Variables

This page was built for publication: Variable selection in high-dimensional partly linear additive models