测试具有功能共变量的条件量子独立性
Yongzhen Feng1, Jie Li2, Xiaojun Song3
1Center for Statistical Science and Department of Industrial Engineering, Tsinghua University, Beijing 100084, China.
Biometrics
|May 14, 2024
概括
这项研究引入了功能数据的新型非参数条件独立性测试,有效地解决了维度的诅咒. 拟议的方法在检测依赖关系方面表现出强大的力量,通过模拟和EEG数据分析进行验证.
科学领域:
- 统计 统计 统计 统计
- 功能数据分析 功能数据分析
- 非参数统计的统计数据.
背景情况:
- 条件独立性测试在统计建模中至关重要.
- 现有的方法经常与高维的功能共变量作斗争.
- 维度的诅咒在功能数据分析中构成了重大挑战.
研究的目的:
- 开发一种新的非参数条件独立性测试,用于标量响应和功能共变量.
- 在功能数据分析中解决维度的诅咒.
- 为分析复杂数据集提供多功能统计工具.
主要方法:
- 克拉默--米塞斯类型测试统计数据是使用经验过程构建的.
- 为了减轻维度性,使用函数共变量的随机投影.
- 非对称的零分布和功率属性是在温和假设下得出的.
- 建议用于临界值估计的乘数引导.
主要成果:
- 提出的测试有效地避免了零假设下的维度的诅咒.
- 测试统计显示了可取的非对称的全球和局部功率属性.
- 它可以检测到以参数速率收的局部替代品.
- 蒙特卡洛模拟证实了有限样本的良好性能.
结论:
- 开发的非参数测试为使用功能共变量进行条件独立性测试提供了强大且计算效率高的解决方案.
- 通过对EEG数据的实际应用来证明该方法的实用性.
- 这种方法在使用功能数据的领域中增强了统计推理.
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