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Articles

Support vector machine with imprecise observation based on pinball loss

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By Qian Liu, YUHONG SHENG
| Taylor & Francis Online
Advanced search Communications in Statistics - Theory and Methods Latest Articles Submit an article Journal homepage Full Article Figures & data References Citations Metrics Reprints & Permissions Read this article /doi/full/10.1080/03610926.2026.2689091?needAccess=true Abstract SVM is widely used in machine learning due to its simplicity, global optimization characteristics, and strong generalization ability.
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Support vector regression with imprecise observations based on Huber loss function

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By Xinyue Zhu, YUHONG SHENG
| Taylor & Francis Online
Advanced search Communications in Statistics - Simulation and Computation Latest Articles Submit an article Journal homepage Full Article Figures & data References Citations Metrics Reprints & Permissions Read this article /doi/full/10.1080/03610918.2026.2643718?needAccess=true Abstract Support vector regression (SVR) and support vector machines (SVM) are learning machines with excellent generalization performance.
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Minimum relative error estimation for uncertain regression model with imprecise observations

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By Xin Huang, YUHONG SHENG
| Taylor & Francis Online
In regression analysis, the least absolute deviation estimation is the most commonly used parameter estimation method in the presence of outliers, and it yields robust estimates by minimizing absolute errors. In practice, experts pay more attention to the relative error because it can intuitively demonstrate the relationship between predicted values and true values, and it is more robust to outliers.
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