scholarly journals Robust regression estimation and inference in the presence of cellwise and casewise contamination

2016 ◽  
Vol 99 ◽  
pp. 1-11 ◽  
Author(s):  
Andy Leung ◽  
Hongyang Zhang ◽  
Ruben Zamar
2014 ◽  
Vol 2014 ◽  
pp. 1-8
Author(s):  
Kuaini Wang ◽  
Jingjing Zhang ◽  
Yanyan Chen ◽  
Ping Zhong

Least squares support vector machine (LS-SVM) is a powerful tool for pattern classification and regression estimation. However, LS-SVM is sensitive to large noises and outliers since it employs the squared loss function. To solve the problem, in this paper, we propose an absolute deviation loss function to reduce the effects of outliers and derive a robust regression model termed as least absolute deviation support vector regression (LAD-SVR). The proposed loss function is not differentiable. We approximate it by constructing a smooth function and develop a Newton algorithm to solve the robust model. Numerical experiments on both artificial datasets and benchmark datasets demonstrate the robustness and effectiveness of the proposed method.


2015 ◽  
Vol 58 (2) ◽  
pp. 505-525 ◽  
Author(s):  
Mohamed Lemdani ◽  
Elias Ould Saïd

2009 ◽  
Vol 13 (3/4) ◽  
pp. 293-321 ◽  
Author(s):  
James B. McDonald ◽  
◽  
Richard A. Michelfelder ◽  
Panayiotis Theodossiou ◽  
◽  
...  

Sign in / Sign up

Export Citation Format

Share Document