一个新的基于p值的多重测试程序,用于通用线性模型
Joseph Rilling1, Cheng Yong Tang1
1Department of Statistics, Operations, and Data Science, Temple University, Philadelphia, PA 19122 USA.
概括
本研究提出了一种新的p值方法,用于一般化的线性模型,以控制依赖测试的错误发现率 (FDR). 它提供了一个灵活的统计框架和高效的算法,用于强大的多重测试.
科学领域:
- 统计 统计 统计 统计
- 统计建模 统计建模
- 计算统计学 计算统计学
背景情况:
- 通用线性模型 (GLMs) 广泛使用,但由于异质变异和参数依赖性,在多重测试中面临挑战.
- 当前的方法在测试统计数据任意依赖时,难以控制错误发现率 (FDR).
研究的目的:
- 为GLMs开发一种基于p值的多重测试方法.
- 为了应对在任意依赖结构下控制FDR的挑战.
- 提供具有高效计算算法的多功能统计框架.
主要方法:
- 为GLM开发基于p值的多重测试框架.
- 整合用于模型矩阵构建的工具,包括随机行顺序和模型X模仿.
- 解决二次矩阵方程的高效算法,用于构建配对的p值,适合两步测试程序.
主要成果:
- 拟议的方法有效地控制在特定水平的错误发现率 (FDR).
- 理论分析证实了新方法的理想特性.
- 经验评估表明,在各种模拟场景中表现强.
结论:
- 这种基于p值的新方法为通用线性模型中的多重测试提供了强大的解决方案.
- 开发的框架和算法提高了FDR控制在复杂的统计环境中的适用性.
- 这种方法为研究人员在GLM中使用依赖测试统计工作提供了有价值的工具.
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