Sequential profile Lasso for ultra-high-dimensional partially linear models
From MaRDI portal
Publication:5880183
DOI10.1080/24754269.2017.1396432OpenAlexW2770292153MaRDI QIDQ5880183
Yujie Li, Tie Jun Tong, Gao Rong Li
Publication date: 7 March 2023
Published in: Statistical Theory and Related Fields (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/24754269.2017.1396432
partially linear modelultra-high-dimensional dataextended Bayesian information criterionscreening propertysequential profile Lasso
Related Items (2)
Balanced estimation for high-dimensional measurement error models ⋮ Nonparametric independence screening for ultra-high dimensional generalized varying coefficient models with longitudinal data
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Model-Free Feature Screening for Ultrahigh-Dimensional Data
- The Adaptive Lasso and Its Oracle Properties
- Profile forward regression screening for ultra-high dimensional semiparametric varying coefficient partially linear models
- Estimation and variable selection for generalized additive partial linear models
- Robust rank correlation based screening
- The sparsity and bias of the LASSO selection in high-dimensional linear regression
- SCAD-penalized regression in high-dimensional partially linear models
- Penalized quasi-likelihood estimation in partial linear models
- Consistent covariate selection and post model selection inference in semiparametric regression.
- Least angle regression. (With discussion)
- High-dimensional graphs and variable selection with the Lasso
- Profiled forward regression for ultrahigh dimensional variable screening in semiparametric partially linear models
- Forward Regression for Ultra-High Dimensional Variable Screening
- Nonparametric Independence Screening in Sparse Ultra-High-Dimensional Additive Models
- Extended Bayesian information criteria for model selection with large model spaces
- Sure Independence Screening for Ultrahigh Dimensional Feature Space
- Feature Screening via Distance Correlation Learning
- Sequential Lasso Cum EBIC for Feature Selection With Ultra-High Dimensional Feature Space
- Nonparametric Independence Screening in Sparse Ultra-High-Dimensional Varying Coefficient Models
- L 1-Regularization Path Algorithm for Generalized Linear Models
- Variable Selection for Partially Linear Models With Measurement Errors
- Model-Free Feature Screening for Ultrahigh Dimensional Discriminant Analysis
- Estimation and variable selection for semiparametric additive partial linear models
- Partially linear additive quantile regression in ultra-high dimension
This page was built for publication: Sequential profile Lasso for ultra-high-dimensional partially linear models