scholarly journals Semisupervised inference for explained variance in high dimensional linear regression and its applications

2020 ◽  
Vol 82 (2) ◽  
pp. 391-419
Author(s):  
T. Tony Cai ◽  
Zijian Guo
Keyword(s):  
Linear Regression ◽  
High Dimensional ◽  
Explained Variance

scholarly journals SPReM: Sparse Projection Regression Model For High-Dimensional Linear Regression

2015 ◽  
Vol 110 (509) ◽  
pp. 289-302 ◽  
Author(s):  
Qiang Sun ◽  
Hongtu Zhu ◽  
Yufeng Liu ◽  
Joseph G. Ibrahim
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
High Dimensional ◽  
Sparse Projection

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.

Empirical likelihood for high-dimensional linear regression models

Metrika ◽  
2013 ◽  
Vol 77 (7) ◽  
pp. 921-945 ◽  
Author(s):  
Hong Guo ◽  
Changliang Zou ◽  
Zhaojun Wang ◽  
Bin Chen
Keyword(s):  
Linear Regression ◽  
Empirical Likelihood ◽  
Regression Models ◽  
High Dimensional ◽  
Linear Regression Models
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