一种强大而高效的变化点检测方法,用于高维线性模型
Zhong-Cheng Han1, Kong-Sheng Zhang2, Yan-Yong Zhao1
1School of Statistics and Mathematics, Nanjing Audit University, Nanjing, People's Republic of China.
Journal of applied statistics
|July 4, 2025
概括
本研究引入了高维数据的模态线性回归,有效地估计了各个子群的不同回归系数. 该方法还执行变量选择和变化点检测,以进行可靠的分析.
科学领域:
- 统计 统计 统计 统计
- 计量经济学 计量经济学 计量经济学
- 机器学习 机器学习
背景情况:
- 在线性模型中,估计回归系数至关重要.
- 不知系数参数可以在不同的数据段中变化.
- 高维共变量在传统的回归分析中带来了挑战.
研究的目的:
- 提出一种新的方法来分析在两个亚群中具有潜在不同的回归系数的线性模型,特别是在高维设置中.
- 引入模态线性回归以对未知的系数参数进行可靠和高效的估计.
- 开发一种能够进行变量选择和变化点检测的方法.
主要方法:
- 引入模态线性回归用于参数估计.
- 变量选择和变化点检测被整合到拟议的方法中.
- 开发了一个结合kick-one-off和SCAD方法的估计算法.
主要成果:
- 拟议的模态线性回归方法提供了稳定性和效率.
- 该方法证明了在变量选择和变化点检测方面的能力.
- 拟议方法的限制性行为是在温和假设下建立的.
结论:
- 模态线性回归为具有不同系数的高维线性模型提供了一种有效的方法.
- 开发的算法通过模拟和真实数据分析促进了实际实施和评估.
- 该研究为分析具有复杂系数结构的细分数据提供了一个多功能工具.
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