PDC-MAKES:一种条件选方法,用于控制高维多响应设置中的错误发现
Wei Xiong1, Han Pan2,3, Tong Shen1
1School of Statistics, University of International Business and Economics, Beijing, 100029, China.
Biometrics
|April 25, 2025
概括
这项研究引入了一种新的无模型方法,用于识别复杂数据集中的重要预测因素. 它有效地控制了错误发现率 (FDR),即使使用高维和相关数据.
科学领域:
- 统计 统计 统计 统计
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 数据中的高维度和强烈的相关性对识别关键预测因素构成挑战.
- 现有的方法在多响应设置和复杂的数据结构方面扎.
研究的目的:
- 为超高维,多响应数据提出一种无模型的条件特征选方法.
- 控制错误发现率 (FDR),同时识别重要的预测因素.
主要方法:
- 使用部分距离相关性来测量随机向量之间的依赖性,控制多变量效应.
- 采用随机化的仿制值来稳定值和错误发现率控制.
- 开发一个强大的选方法,用于重尾数据和相关预测器.
主要成果:
- 该方法识别了与反应略有无关但有条件相关的预测因素.
- 实现可靠的选属性,保持FDR控制,并提供更高的统计能力.
- 对重尾分布和高预测器相关性表现出强度.
结论:
- 拟议的方法为超高维设置中的特征选提供了一种强大而稳健的方法.
- 它通过允许对高维变量进行条件化来推进当前的研究.
- 该方法在模拟和现实数据应用中显示出卓越的性能.
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