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When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
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过拒绝集,同时保持错误发现率控制.

Eugene Katsevich1, Chiara Sabatti2, Marina Bogomolov3

  • 1Department of Statistics, University of Pennsylvania.

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|June 22, 2023
PubMed
概括
此摘要是机器生成的。

我们介绍了Focused BH,这是一种调整多重测试程序的方法,当假设具有像ICD或GO这样的结构时. 这确保了即使过后也可靠的结果,保持了错误发现率的控制.

关键词:
基因本体学丰富分析分析定向非循环图是指向的非循环图.外部节点是指外部节点.一个全现象的关联研究研究.结构化的多重测试.树木树木的树木树木的树木.

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科学领域:

  • 统计方法学的统计方法.
  • 生物信息学是一种生物信息学.
  • 基因组学就是基因组学.

背景情况:

  • 假设结构 (例如,ICD,GO) 在多重测试中产生冗余.
  • 过假设的拒绝可以使统计保证无效.
  • 解释性受到冗余拒绝的阻碍.

研究的目的:

  • 提出Focused BH,一种在应用预先规定的过器后调整多次测试的原则方法.
  • 确保在使用特定领域的假设结构时控制错误发现率.
  • 提供适用于各种过场景的灵活方法.

主要方法:

  • 开发了Focused BH方法,以结合预先规定的过器.
  • 在特定条件下提供理论证明,以控制错误发现率.
  • 进行模拟以评估各种设置中的性能.

主要成果:

  • 聚焦的BH保持了错误发现率的控制,即使使用单调的过器和积极依赖的p值.
  • 模拟显示了Focused BH在各种场景中的强表现.
  • 该方法在使用ICD和GO数据的现实世界分析中被证明是实用的.

结论:

  • 专注的BH提供了一种可靠的方法,用于通过结构化假设和过来进行多重测试.
  • 这种方法提高了结果的解释性,而不会影响统计学有效性.
  • 专注的BH适用于具有结构化数据的各种科学领域.