Bibliographic Details
| Title: |
LIC for Distributed Censored Regression. |
| Authors: |
Zhang, Chuanqi1 Zhangcq0601@163.com, Guo, Guangbao2 ggb1111111@163.com |
| Source: |
IAENG International Journal of Applied Mathematics. Jul2026, Vol. 56 Issue 7, p2603-2611. 9p. |
| Subjects: |
Distributed computing, Censoring (Statistics), Big data, Robust statistics |
| Abstract: |
We introduce a novel distributed censored regression that combines the flexibility of censored distributions with the efficiency of distributed computing, effectively addressing the challenges associated with large-scale censored datasets. Within this framework, we propose an optimal subset selection criterion 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 distributed censored regression models, with experimental data further corroborating its robustness and stability. [ABSTRACT FROM AUTHOR] |
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| Database: |
Engineering Source |
