Two-step variable selection in partially linear additive models with time series data

DOI10.1080/03610918.2016.1259477OpenAlexW2550458737MaRDI QIDQ5084730

Mu Feng, Zhao Chen, Xi-ming Cheng

Publication date: 28 June 2022

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

Full work available at URL: https://doi.org/10.1080/03610918.2016.1259477

zbMATH Keywords

additive modelsvariable selectionGCVadaptive LASSOgroup LASSOorthogonal series approximation

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Nonparametric estimation (62G05)

Related Items (1)

Asymptotics of estimators for nonparametric multivariate regression models with long memory

Cites Work

• Unnamed Item
• The Adaptive Lasso and Its Oracle Properties
• One-step sparse estimates in nonconcave penalized likelihood models
• The sparsity and bias of the LASSO selection in high-dimensional linear regression
• Variable selection in nonparametric additive models
• From Stein's unbiased risk estimates to the method of generalized cross- validation
• Least angle regression. (With discussion)
• Nonlinear Time Series
• Varying coefficient models for data with auto-correlated error process
• Semiparametric Regression Smoothing of Non-linear Time Series
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Model Selection and Estimation in Regression with Grouped Variables

This page was built for publication: Two-step variable selection in partially linear additive models with time series data