scholarly journals An Improved Forward Regression Variable Selection Algorithm for High-Dimensional Linear Regression Models

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 129032-129042
Author(s):  
Yanxi Xie ◽  
Yuewen Li ◽  
Zhijie Xia ◽  
Ruixia Yan
Keyword(s):  
Linear Regression ◽  
Variable Selection ◽  
Regression Models ◽  
High Dimensional ◽  
Linear Regression Models ◽  
Selection Algorithm

Early stopping aggregation in selective variable selection ensembles for high-dimensional linear regression models

2018 ◽  
Vol 153 ◽  
pp. 1-11 ◽  
Author(s):  
Chun-Xia Zhang ◽  
Jiang-She Zhang ◽  
Qing-Yan Yin
Keyword(s):  
Linear Regression ◽  
Variable Selection ◽  
Regression Models ◽  
High Dimensional ◽  
Linear Regression Models ◽  
Early Stopping

scholarly journals A One-Covariate at a Time, Multiple Testing Approach to Variable Selection in High-Dimensional Linear Regression Models

2016 ◽  
Vol 2016 (290) ◽  
Author(s):  
Alexander Chudik ◽  
◽  
George Kapetanios ◽  
M. Hashem Pesaran ◽  
◽  
...  
Keyword(s):  
Linear Regression ◽  
Variable Selection ◽  
Regression Models ◽  
Multiple Testing ◽  
High Dimensional ◽  
Linear Regression Models ◽  
Testing Approach

scholarly journals A One Covariate at a Time, Multiple Testing Approach to Variable Selection in High-Dimensional Linear Regression Models

Econometrica ◽  
2018 ◽  
Vol 86 (4) ◽  
pp. 1479-1512 ◽  
Author(s):  
A. Chudik ◽  
G. Kapetanios ◽  
M. Hashem Pesaran
Keyword(s):  
Linear Regression ◽  
Variable Selection ◽  
Regression Models ◽  
Multiple Testing ◽  
High Dimensional ◽  
Linear Regression Models ◽  
Testing Approach

scholarly journals Post-l1-penalized estimators in high-dimensional linear regression models

2010 ◽  
Author(s):  
Victor Chernozhukov ◽  
Alexandre Belloni
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
High Dimensional ◽  
Linear Regression Models

Sequential Model Averaging for High Dimensional Linear Regression Models

2017 ◽  
Author(s):  
Wei Lan ◽  
Yingying Ma ◽  
Junlong Zhao ◽  
Hansheng Wang ◽  
Chih-Ling Tsai
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Model Averaging ◽  
High Dimensional ◽  
Linear Regression Models ◽  
Sequential Model

scholarly journals Seagull: lasso, group lasso and sparse-group lasso regularization for linear regression models via proximal gradient descent

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Jan Klosa ◽  
Noah Simon ◽  
Pål Olof Westermark ◽  
Volkmar Liebscher ◽  
Dörte Wittenburg
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Gradient Descent ◽  
Methylation Status ◽  
R Package ◽  
Group Lasso ◽  
High Dimensional ◽  
Linear Regression Models ◽  
Sparse Group Lasso ◽  
Proximal Gradient Descent

Abstract Background Statistical analyses of biological problems in life sciences often lead to high-dimensional linear models. To solve the corresponding system of equations, penalization approaches are often the methods of choice. They are especially useful in case of multicollinearity, which appears if the number of explanatory variables exceeds the number of observations or for some biological reason. Then, the model goodness of fit is penalized by some suitable function of interest. Prominent examples are the lasso, group lasso and sparse-group lasso. Here, we offer a fast and numerically cheap implementation of these operators via proximal gradient descent. The grid search for the penalty parameter is realized by warm starts. The step size between consecutive iterations is determined with backtracking line search. Finally, seagull -the R package presented here- produces complete regularization paths. Results Publicly available high-dimensional methylation data are used to compare seagull to the established R package SGL. The results of both packages enabled a precise prediction of biological age from DNA methylation status. But even though the results of seagull and SGL were very similar (R2 > 0.99), seagull computed the solution in a fraction of the time needed by SGL. Additionally, seagull enables the incorporation of weights for each penalized feature. Conclusions The following operators for linear regression models are available in seagull: lasso, group lasso, sparse-group lasso and Integrative LASSO with Penalty Factors (IPF-lasso). Thus, seagull is a convenient envelope of lasso variants.

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