对于高维半参数模型的联合双机器学习
Kai Kang1, Zhihao Wu2, Xinjie Qian3
1Department of Statistics, Sun Yat-sen University, Guangzhou, Guangdong 510275, China.
Biometrics
|November 21, 2025
概括
本研究引入了一个联合的双重机器学习框架,用于用本地化数据训练全球模型,在多中心研究的高维半参数模型中有效处理复杂的麻烦参数.
科学领域:
- 机器学习 机器学习
- 统计建模 统计建模
- 生物统计学 生物统计学
背景情况:
- 联合学习训练全球模型与去中心化数据,但与高维半参数模型和麻烦参数扎.
- 多中心研究由于数据异质性和需要进行保护隐私的分析而存在独特的挑战.
研究的目的:
- 为多中心研究中的半参数模型提出一个新的联合双机器学习框架.
- 为了应对高维的麻烦参数在联合设置中所带来的挑战.
- 开发一个强大的联合估计器,将本地和汇总数据结合起来.
主要方法:
- 利用双重机器学习 (DML) 进行可靠的参数估计.
- 在Neyman-orthogonal框架内扩展代用高效分数方法.
- 应用密度比倾斜用于联合估计器构建,将个人数据与总结统计数据集成.
主要成果:
- 拟议的方法有效地减轻了规范化偏差和过拟合在高维的麻烦参数估计.
- 估计器的限制分布是根据最小假设建立的.
- 通过广泛的模拟和对阿尔茨海默病神经成像计划数据的现实应用来验证.
结论:
- 联合的双重机器学习框架为分析多中心研究中的复杂数据提供了强大的解决方案.
- 这种方法提高了半参数模型的联合学习的准确性和可靠性.
- 显示了各种领域的应用,需要维护隐私,高维度数据分析的巨大潜力.
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