距离加权定向回归对于Fréchet的足够尺寸缩小
Chao Ying1, Zhou Yu2, Xin Zhang3
1Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53705, United States.
Biometrics
|May 7, 2025
概括
本研究引入了距离加权定向回归,用于分析复杂的非欧几里德数据. 该方法统一了各种数据类型的足够维度缩小,改善了长寿和健康研究中的预测和解释.
科学领域:
- 统计 统计 统计 统计
- 数据科学数据科学数据科学
- 减小尺寸性的减小方法
背景情况:
- 对非欧几里得数据的分析在人类寿命和大脑网络研究等领域至关重要.
- 弗雷切足够维度缩小 (FSDR) 试图揭示复杂的对象值响应和预测器之间的关系,同时减少预测器的维度.
研究的目的:
- 为线性和非线性Fréchet足够尺寸缩小引入统一的距离加权定向回归方法.
- 扩展经典的定向回归框架来处理非欧几里德数据.
主要方法:
- 开发了一种用于定向回归的新型距离权重方法.
- 制定了适用于欧几里德和非欧几里德反应的统一方法.
- 将该方法扩展到非线性FSDR.
主要成果:
- 导出了线性FSDR估计器的非对称正常值.
- 确定非线性FSDR估计器的收率.
- 模拟研究证实了经验性能和理论发现.
结论:
- 拟议的距离加权定向回归为FSDR提供了一个统一的方法.
- 该方法增强了解释和样本外预测,正如人类死亡率和糖尿病患病率分析所证明的那样.
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