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Updated: Sep 16, 2025

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在物流回归和投资组合优化中应用规范化协差矩阵
1College of Science, Civil Aviation University of China, Tianjin, 300300, China. sunfang2005@163.com.
Scientific reports
|July 4, 2025
概括
本研究引入了一种新的规范化协差估计方法,用于解决高维数据中的非可逆性问题. 该方法增强了后勤回归和投资组合优化,提高了模型稳定性和准确性.
科学领域:
- 统计 统计 统计 统计
- 机器学习 机器学习
- 金融数学 金融数学
背景情况:
- 协差估计对于物流回归和投资组合优化至关重要.
- 高维或小样本数据往往导致非可逆共变矩阵,阻碍模型性能.
- 传统方法与不可逆的共变矩阵作斗争,限制了它们的适用性.
研究的目的:
- 开发一种新的规范化协差估计方法.
- 为了解决不可逆共变矩阵的关键问题.
- 提高协差估计的数值稳定性和可靠性.
主要方法:
- 开发了一种新的规范化协差估计技术.
- 该方法被整合到逻辑回归的分析解决方案框架中.
- 提出的方法应用于投资组合回报管理.
主要成果:
- 拟议的方法确保了估计的协差矩阵的可逆性.
- 整合到物流回归中显著改善了分析解决方案的稳定性和准确性.
- 该方法提高了金融应用中的优化解决方案的质量.
- 实验结果显示,在物流回归和投资组合优化方面,与传统方法相比,在物流回归和投资组合优化方面表现优越.
结论:
- 新的规范化协差估计方法有效地克服了非可逆性问题.
- 该方法为物流回归和投资组合优化提供了更高的稳定性和准确性.
- 该方法在金融应用中展示了实际价值和稳定性.
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