Nonconvex penalized ridge estimations for partially linear additive models in ultrahigh dimension

DOI10.1016/j.stamet.2015.03.001zbMath1487.62080OpenAlexW2001043271MaRDI QIDQ1731372

Mingqiu Wang

Publication date: 13 March 2019

Published in: Statistical Methodology (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.stamet.2015.03.001

zbMATH Keywords

ridge regressionmulticollinearityoracle propertyhigh dimensionpartially linear additive modelsnonconvex penalties

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Ridge regression; shrinkage estimators (Lasso) (62J07) Linear regression; mixed models (62J05)

Related Items

Defective models induced by gamma frailty term for survival data with cured fraction ⋮ An efficient estimation for the parameter in additive partially linear models with missing covariates ⋮ Shrinkage estimation for identification of linear components in composite quantile additive models ⋮ Profile statistical inference for partially linear additive models with a diverging number of parameters ⋮ Augmented-limited regression models with an application to the study of the risk perceived using continuous scales

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Coordinate descent algorithms for nonconvex penalized regression, with applications to biological feature selection
• Nearly unbiased variable selection under minimax concave penalty
• Profiled adaptive elastic-net procedure for partially linear models with high-dimensional covar\-i\-ates
• Group selection in high-dimensional partially linear additive models
• Semiparametric regression models with additive nonparametric components and high dimensional parametric components
• The sparse Laplacian shrinkage estimator for high-dimensional regression
• Spline-backfitted kernel smoothing of partially linear additive model
• Automatic model selection for partially linear models
• The sparsity and bias of the LASSO selection in high-dimensional linear regression
• SCAD-penalized regression in high-dimensional partially linear models
• Additive regression and other nonparametric models
• Local asymptotics for regression splines and confidence regions
• Simultaneous analysis of Lasso and Dantzig selector
• On the adaptive elastic net with a diverging number of parameters
• Variable selection via combined penalization for high-dimensional data analysis
• Bridge Estimators in the Partially Linear Model with High Dimensionality
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Sure Independence Screening for Ultrahigh Dimensional Feature Space
• Group variable selection via SCAD-L₂
• Variable Selection for Partially Linear Models With Measurement Errors
• Regularization and Variable Selection Via the Elastic Net
• Smoothly Clipped Absolute Deviation on High Dimensions
• On the Non-Negative Garrotte Estimator
• Ridge Regression: Biased Estimation for Nonorthogonal Problems
• Estimation and variable selection for semiparametric additive partial linear models
• A practical guide to splines.