基于加权的皮尔森相关系数测试统计数据的推理程序
1Department of Biostatistics and Bioinformatics, Roswell Park Cancer Institute, Buffalo, NY, USA.
Journal of applied statistics
|February 19, 2024
概括
在使用加权的皮尔森相关性时,常见的t-test会膨胀I型错误. 一个学生化的顺序测试可稳定地控制错误,即使是小样本和非正常数据.
科学领域:
- 统计 统计 统计 统计
- 生物统计学 生物统计学
背景情况:
- t测试被广泛用于假设测试.
- 在各种分析中使用加权皮尔森相关性.
- 现有的t-测试方法显示,I型错误控制与加权Pearson相关性差.
研究的目的:
- 为了评估加权皮尔森相关性 t 试验的 I 型错误控制.
- 在这种情况下,提出用于准确测试假设的新方法.
主要方法:
- 导出了加权Pearson相关系数的大样本方差.
- 开发了一种非对称测试和学生化的变量测试.
- 进行了广泛的模拟研究,采用不同的样本大小和分布.
主要成果:
- 标准t-test显示了严重膨胀的I型错误率.
- 拟议的学生化变换测试,特别是使用费舍尔的Z统计,有效地控制了I型错误.
- 即使在小样本大小和非正常数据场景中也观察到强的表现.
结论:
- 学生化变换测试提供了一个可靠的解决方案,用于测试假设与加权的皮尔森相关性.
- 这种方法确保了准确的统计推断,解决了标准t测试的局限性.
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