Controlling Positive False Discovery Rate with Power

用力量控制阳性错误发现率

• 批准号: 0706048
• 负责人: Zhiyi Chi
• 金额: $ 12万
• 依托单位: University of Connecticut
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-06-01 至 2011-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0706048&HistoricalAwards=false
• 关键词: Controlling; Positive; False; Discovery; Rate

This research proposal aims to develop theoretical and methodological tools to improve error rate control and power for multiple hypothesis testing. The centerpiece of the investigation is the False Discovery Rate (FDR) and its important relative, the so-called positive FDR (pFDR). First, in order to evaluate rejected nulls or discoveries more objectively, the PI will develop methods to estimate the pFDR for multiple testing as well as methods to set criteria for test statistics in order to make sure they have enough information to attain desired pFDR control. Second, the PI will develop FDR control based on multivariate statistics. Although using multivariate statistics for multiple testing is widely seen in many areas, there has been little work on this topic in the current literature on FDR control. The PI will develop different FDR controlling procedures that incorporate multivariate statistics and investigate how to combine the information in the statistics effectively in order to achieve pFDR control with optimal power.Multiple hypothesis testing provides a statistical foundation for massive data analysis and knowledge finding in a wide range of areas of modern science and technology, including neuroscience, brain imaging, genomics and imagery processing. A fundamental challenge in these areas is to obtain true discoveries while avoiding false discoveries. The project will generate various tools to reach this goal. It will enable researchers to evaluate discoveries more carefully and to avoid potential pitfalls in their data collection and analysis. Moreover, it will provide researchers with a large collection of statistical methods to find true discoveries more efficiently.

本研究旨在开发理论和方法工具，以提高错误率控制和多重假设检验的能力。调查的核心是错误发现率（FDR）及其重要的相对值，即所谓的正FDR（pFDR）。首先，为了更客观地评价被拒绝的零值或发现，PI将开发用于估计多次检测的pFDR的方法以及用于设定检测统计标准的方法，以确保其具有足够的信息来实现所需的pFDR控制。其次，PI将基于多元统计开发FDR控制。虽然使用多元统计进行多重检验在许多领域被广泛看到，但在FDR控制的当前文献中，关于这个主题的工作很少。 PI将开发不同的FDR控制程序，包括多元统计，并研究如何有效地将统计中的信息联合收割机组合，以实现具有最佳功效的pFDR控制。多假设检验为现代科学和技术的广泛领域，包括神经科学，脑成像，基因组学和图像处理的大量数据分析和知识发现提供了统计基础。 这些领域的一个根本挑战是获得真正的发现，同时避免错误的发现。该项目将产生各种工具来实现这一目标。 它将使研究人员能够更仔细地评估发现，并避免数据收集和分析中的潜在陷阱。 此外，它将为研究人员提供大量的统计方法，以更有效地找到真正的发现。