集群之间和集群内部的斯皮尔曼等级相关性
Shengxin Tu1, Chun Li2, Bryan E Shepherd1
1Department of Biostatistics, Vanderbilt University, Nashville, Tennessee, USA.
Statistics in medicine
|January 24, 2025
概括
这项研究为集群数据引入了新的非参数的斯皮尔曼等级相关度,为皮尔森系数提供了强大的替代方案. 这些方法提供了对集群内部和集群之间的相关性更全面的了解.
科学领域:
- 生物统计学 生物统计学
- 统计建模 统计建模
- 数据分析 数据分析
背景情况:
- 在纵向和分组研究中常见的集群数据需要专门的相关性分析.
- 对于集群数据,现有的皮尔森相关系数对异常值和数据转换敏感.
- 目前用于集群数据的非参数方法仅限于总相关性.
研究的目的:
- 定义群体参数,用于集群之间的和集群内部的斯皮尔曼等级相关性.
- 将非参数相关性分析扩展到聚类数据,解决皮尔森系数的局限性.
- 为偏斜或顺序集群数据提供强大的相关性测量.
主要方法:
- 定义了群体参数,用于集群之间的和集群内部的斯皮尔曼等级相关性,作为皮尔森系数的扩展.
- 开发了一个理论框架,展示了总的斯皮尔曼相关性作为集群之间的和集群内相关性的组合.
- 建议的估计和推断方法,通过模拟和现实世界数据分析进行验证.
主要成果:
- 成功定义和扩展斯皮尔曼等级相关性,以解释聚类数据结构.
- 证明总的斯皮尔曼等级相关性是集群内和集群之间斯皮尔曼相关性的加权组合.
- 确定了集群内部的斯皮尔曼等级相关性和与共变量调整的部分斯皮尔曼等级相关性之间的等价性.
结论:
- 拟议的斯皮尔曼等级相关性指标为分析集群数据提供了强大而多用途的方法.
- 这些方法克服了皮尔森相关性的局限性,特别是对于非正常分布或顺序数据.
- 这些发现适用于使用集群数据的不同领域,提高了相关性分析的准确性.
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