在线测试多重假设测试
David S Robertson1, James M S Wason2, Aaditya Ramdas3
1MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.
概括
本研究回顾了在线假设测试中控制错误发现率 (FDR) 的方法,其中数据顺序到达. 它提供了一个全面的理论指南,应用程序和算法,用于管理实时数据流中的统计错误.
科学领域:
- 统计 统计 统计 统计
- 数据科学数据科学数据科学
- 生物信息学是一种生物信息学.
背景情况:
- 大规模的假设测试在现代数据分析中很常见.
- 控制错误发现率 (FDR) 对于可靠的结果至关重要.
- 传统的FDR方法假设所有数据都同时可用,不适合在线,连续测试.
研究的目的:
- 为在多重假设测试中进行在线错误率控制的方法提供全面的审查.
- 为了弥合传统的线下FDR控制和顺序数据分析的需求之间的差距.
- 为在线假设测试提供理论基础,实际应用和算法比较的见解.
主要方法:
- 在过去15年中开发的在线错误率控制方法的文献综述.
- 在线多重假设测试中的关键理论概念的阐述.
- 模拟研究比较不同在线测试算法的性能.
主要成果:
- 识别和综合了在线FDR控制方法的关键进展.
- 通过应用示例展示了在线测试框架的实际适用性.
- 提供了各种在线假设测试算法的比较性能分析.
结论:
- 在线错误率控制是顺序数据分析的关键和不断发展的领域.
- 审查的方法为管理实时假设测试中的统计错误提供了有效的策略.
- 这项工作是研究人员和从业人员应用这些先进的统计技术的宝贵资源.
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