控制树基聚合中的错误分割率
Simeng Shao1, Jacob Bien2, Adel Javanmard2
1Amazon, Seattle.
Journal of the American Statistical Association
|July 18, 2025
概括
这项研究引入了一种新的错误度量,即错误分割率,用于识别层次数据中的适当子组. 介绍了一种算法,用于控制基于树的聚合率,改进跨各种科学领域的数据分析.
科学领域:
- 数据科学数据科学数据科学
- 统计建模 统计建模
- 计算生物学 计算生物学
背景情况:
- 层次数据结构在各个科学领域都很常见,从商业部门到微生物分类学.
- 分析此类数据的现有方法往往难以确定适当的聚合水平.
- 需要强大的方法来识别树定义数据中的有意义的子组.
研究的目的:
- 引入和定义一种新的误差测量方法,即错误分割率,用于基于树的数据聚合.
- 开发和验证一个多重假设测试算法来控制这个错误率.
- 在涉及平均值和回归系数聚合的场景中证明算法的有效性.
主要方法:
- 虚假分割率的定义是指在层次数据中不适当地分割子组的度量.
- 为树结构量身定制的多重假设测试算法的开发.
- 理论证明了算法的控制错误分割率的能力.
- 在以树为基础的数据集中对平均值和回归系数的聚合的应用.
主要成果:
- 错误分裂率被证明与一般树结构中的错误发现率不同.
- 拟议的算法有效地控制了虚假分割率.
- 该方法适用于各种基于树的聚合问题,包括平均值和回归系数.
结论:
- 错误分割率为基于树的聚合提供了比传统方法更合适的错误度量.
- 开发的算法提供了一个统计学上合理的方法,用于在层次数据中识别子组.
- 这项工作增强了各种科学领域复杂,结构化的数据集的分析.
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