Two tales of variable selection for high dimensional regression: Screening and model building

2014 ◽  
Vol 7 (2) ◽  
pp. 140-159 ◽  
Author(s):  
Cong Liu ◽  
Tao Shi ◽  
Yoonkyung Lee
Keyword(s):  
Variable Selection ◽  
Model Building ◽  
High Dimensional ◽  
High Dimensional Regression ◽  
Selection For

Consistent variable selection in high dimensional regression via multiple testing

2006 ◽  
Vol 136 (12) ◽  
pp. 4349-4364 ◽  
Author(s):  
Florentina Bunea ◽  
Marten H. Wegkamp ◽  
Anna Auguste
Keyword(s):  
Variable Selection ◽  
Multiple Testing ◽  
High Dimensional ◽  
High Dimensional Regression

scholarly journals A Survey of Tuning Parameter Selection for High-Dimensional Regression

2020 ◽  
Vol 7 (1) ◽  
pp. 209-226 ◽  
Author(s):  
Yunan Wu ◽  
Lan Wang
Keyword(s):  
Standard Technique ◽  
Penalized Regression ◽  
Optimal Choice ◽  
Parameter Selection ◽  
Tuning Parameter ◽  
High Dimensional ◽  
Design Matrix ◽  
High Dimensional Regression ◽  
Selection For ◽  
Sparsity Level

Penalized (or regularized) regression, as represented by lasso and its variants, has become a standard technique for analyzing high-dimensional data when the number of variables substantially exceeds the sample size. The performance of penalized regression relies crucially on the choice of the tuning parameter, which determines the amount of regularization and hence the sparsity level of the fitted model. The optimal choice of tuning parameter depends on both the structure of the design matrix and the unknown random error distribution (variance, tail behavior, etc.). This article reviews the current literature of tuning parameter selection for high-dimensional regression from both the theoretical and practical perspectives. We discuss various strategies that choose the tuning parameter to achieve prediction accuracy or support recovery. We also review several recently proposed methods for tuning-free high-dimensional regression.

Sparse Bayesian variable selection for classifying high-dimensional data

2018 ◽  
Vol 11 (2) ◽  
pp. 385-395 ◽  
Author(s):  
Aijun Yang ◽  
Heng Lian ◽  
Xuejun Jiang ◽  
Pengfei Liu
Keyword(s):  
Variable Selection ◽  
High Dimensional Data ◽  
Bayesian Variable Selection ◽  
High Dimensional ◽  
Selection For
Sign in / Sign up

Export Citation Format

Share Document