用随机p值对离散数据进行复合零假设的多重测试
Daniel Ochieng1, Anh-Tuan Hoang1, Thorsten Dickhaus1
1Institute for Statistics, University of Bremen, Bremen, Germany.
Biometrical journal. Biometrische Zeitschrift
|October 19, 2023
概括
随机的p值为统计测试中的保守性提供了一个解决方案,特别是在离散测试统计数据或非最小有利的参数配置的情况下. 这些新的方法在替代假设下保持有效性并提高功率.
科学领域:
- 统计 统计 统计 统计
- 假设测试 假设测试
背景情况:
- 来自连续测试统计数据的P值在零假设下通常是统一的.
- 在p值中的保守性源于离散测试统计数据或非最小有利参数配置 (LFCs).
研究的目的:
- 介绍和评估使用随机p值的两种新方法.
- 为了解决离散统计和复合无数的假设测试中的保守性.
主要方法:
- 开发了两个随机的p值方法.
- 在二项式和组测试模型下对复合无假设测试的应用.
- 在指数数组内对离散统计模型的随机p值的验证.
主要成果:
- 与非随机版本相比,在零假设下,拟议的随机p值在零假设下是不那么保守的.
- 随机的p值在替代假设下是随机的,不是更小的,表明维持或改善的功率.
- 在各种离散统计模型中证明随机p值的有效性.
结论:
- 随机的p值有效地减轻了假设测试中的保守性.
- 提出的方法是有效的,在统计能力方面具有优势.
- 这些方法适用于复杂的统计模型和设计.
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