LIC for Distributed Skewed Regression

Authors

  • Hengxin Gao, Guangbao Guo Shandong University of Technology, Zibo, China Author

Abstract

We introduce a novel distributed skewed regression that combines the flexibility of skewed distributions with the efficiency of distributed computing, effectively addressing the challenges associated with large-scale skewed datasets. Within this framework, we propose an optimal subset selection cri terion named LIC. Comparative analysis with two widely used metrics demonstrates that LIC achieves superior stability and sensitivity in reducing estimation errors. In addition, we evaluate the applicability of the LIC to various skewed regres sion models, with experimental data further corroborating its robustness and stability.

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Published

2025-09-01

How to Cite

LIC for Distributed Skewed Regression. (2025). IAENG International Journal of Applied Mathematics, 55(9), 2925-2930. https://ijesworld.com/index.php/IEANG/article/view/88