Variable selection in partially linear additive models for modal regression

2017 ◽  
Vol 46 (7) ◽  
pp. 5646-5665 ◽  
Author(s):  
Jing Lv ◽  
Hu Yang ◽  
Chaohui Guo
Keyword(s):  
Variable Selection ◽  
Additive Models ◽  
Partially Linear ◽  
Modal Regression

scholarly journals Error Bound of Mode-Based Additive Models

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 651
Author(s):  
Hao Deng ◽  
Jianghong Chen ◽  
Biqin Song ◽  
Zhibin Pan
Keyword(s):  
Variable Selection ◽  
Hilbert Spaces ◽  
Regression Models ◽  
Reproducing Kernel ◽  
Additive Models ◽  
Generalization Error ◽  
Proposed Model ◽  
Modal Regression ◽  
Kernel Hilbert Spaces ◽  
Non Gaussian

Due to their flexibility and interpretability, additive models are powerful tools for high-dimensional mean regression and variable selection. However, the least-squares loss-based mean regression models suffer from sensitivity to non-Gaussian noises, and there is also a need to improve the model’s robustness. This paper considers the estimation and variable selection via modal regression in reproducing kernel Hilbert spaces (RKHSs). Based on the mode-induced metric and two-fold Lasso-type regularizer, we proposed a sparse modal regression algorithm and gave the excess generalization error. The experimental results demonstrated the effectiveness of the proposed model.

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