modal regression
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2021 ◽  
pp. 109256
Author(s):  
Azzi Amel ◽  
Laksaci Ali ◽  
Ould Saïd Elias


2021 ◽  
Vol 160 ◽  
pp. 107225
Author(s):  
Kangning Wang ◽  
Shaomin Li


Author(s):  
André F. B. Menezes ◽  
Josmar Mazucheli ◽  
Subrata Chakraborty


Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 651
Author(s):  
Hao Deng ◽  
Jianghong Chen ◽  
Biqin Song ◽  
Zhibin Pan

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.



2021 ◽  
Vol 157 ◽  
pp. 107158
Author(s):  
Jianhong Shi ◽  
Yujing Zhang ◽  
Ping Yu ◽  
Weixing Song


Author(s):  
Tao Zhang ◽  
Kengo Kato ◽  
David Ruppert




2021 ◽  
Vol 60 (1) ◽  
pp. 261-308 ◽  
Author(s):  
Aman Ullah ◽  
Tao Wang ◽  
Weixin Yao


2020 ◽  
Vol 50 (10) ◽  
pp. 4393-4405 ◽  
Author(s):  
Yulong Wang ◽  
Yuan Yan Tang ◽  
Luoqing Li ◽  
Hong Chen


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