LIC for Distributed Skewed Regression
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
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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