Collaborative Research: New Directions for Research on Some Large-Scale Multiple Testing Problems
协作研究:一些大规模多重测试问题研究的新方向
基本信息
- 批准号:1309273
- 负责人:
- 金额:$ 12.66万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Continuing Grant
- 财政年份:2013
- 资助国家:美国
- 起止时间:2013-07-15 至 2017-06-30
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
The paradigm shift of hypothesis testing from single to multiple hypotheses, often large number of them, in statistical applications to modern scientific investigations, such as brain imaging, microarray analysis, astronomy, atmospheric science, drug discovery and many others, has generated tremendous upsurge of research in the field of large-scale multiple testing in the last one and half decades. Nevertheless, some fundamentally important theoretical as well as methodological issues arising in many of these investigations still remain to be fully addressed before developing the necessary statistical tools. For instance, in clinical pharmacogenomics involving multiple testing, methods controlling false discoveries are yet to be developed in non-asymptotic setting when these hypotheses are tested group sequentially which is often required in order to meet economical and ethical concerns, or when these hypotheses belong to tree-structured hierarchical families which often happens due to importance based ordering of different sets of hypotheses. Also, in many practical applications of multiple testing where the order in which the tests are to be performed is pre-specified or can be assessed based on available data, but the potential improvements of the existing FDR methodologies exploiting this pre-ordering are yet to be explored. The project seeks to develop new and innovative multiple testing methods tackling these outstanding and related issues by focusing on the following three broad areas of research: (i) group sequential multiple testing, (ii) fixed sequence multiple testing, and (iii) testing multiple families of hypotheses. The project covers a wide spectrum of important multiple testing problems statisticians face in many practical settings. These problems are new and pose several technical challenges, as the existing theory and methodologies on multiple testing controlling false discoveries need to be extended from the framework of single stage or single family to that of multiple stages or multiple families. The proposed research has the potential to open up the door for research on multiple testing in newer directions. It not only aims at advancing the theory of multiple testing but also pays special attention to applications of the developed theories. This project is expected to have a broad impact on the theory and practice of statistics. It aims at modernizing the field of statistics by advancing research in areas of importance in modern scientific experiments, and thus can benefit the society. For instance, the project can potentially pave the way for novel techniques to address statistical issues faced in modern drug discoveries and biomedical experiments. It would also benefit education through training of graduate students and incorporation of the developed methodologies in statistics courses. The results will be disseminated through presentations and discussions at national and international conferences, and visits to other institutions. The software to be developed under this project will be made available, free of charge, to the scientific community.
在过去的15年里,假设检验的范式从单一假设向多个假设(通常是大量假设)的转变,在统计应用到现代科学调查(如脑成像、微阵列分析、天文学、大气科学、药物发现等)中产生了大规模多重检验领域的研究热潮。然而,在开发必要的统计工具之前,许多这些调查中出现的一些根本性的重要理论和方法问题仍有待充分解决。例如,在涉及多个测试的临床药物基因组学中,当这些假设按顺序进行测试时,控制错误发现的方法尚未在非渐近设置中开发,这通常是为了满足经济和伦理问题,或者当这些假设属于树状结构的层次家族时,这通常是由于不同假设集的重要性排序而发生的。此外,在许多实际应用中的多个测试的顺序进行测试是预先指定的,或可以根据现有的数据进行评估,但潜在的改进现有的FDR方法,利用这种预先排序还有待探讨。该项目旨在开发新的和创新的多重测试方法,通过专注于以下三个广泛的研究领域来解决这些悬而未决的相关问题:(i)组序贯多重测试,(ii)固定序列多重测试,以及(iii)测试多个假设家族。该项目涵盖了统计学家在许多实际环境中面临的各种重要的多重测试问题。这些问题是新的,并提出了一些技术挑战,因为现有的理论和方法的多个测试控制错误的发现需要从单阶段或单家庭的框架扩展到多阶段或多家庭。拟议的研究有可能打开大门,研究在新的方向多重测试。它不仅致力于发展多元检验理论,而且特别关注所发展理论的应用。预计该项目将对统计理论和实践产生广泛影响。它旨在通过推进现代科学实验重要领域的研究来实现统计领域的现代化,从而造福社会。例如,该项目可能为新技术铺平道路,以解决现代药物发现和生物医学实验中面临的统计问题。它还将通过培训研究生和在统计课程中纳入所制定的方法来促进教育。结果将通过国家和国际会议上的演讲和讨论以及对其他机构的访问来传播。在该项目下开发的软件将免费提供给科学界。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Sanat Sarkar其他文献
Sanat Sarkar的其他文献
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{{ truncateString('Sanat Sarkar', 18)}}的其他基金
Novel p-Value Based Multiple Testing Methods for Variable Selection with False Discovery Rate Control
基于 p 值的新颖变量选择多重测试方法以及错误发现率控制
- 批准号:
2210687 - 财政年份:2022
- 资助金额:
$ 12.66万 - 项目类别:
Standard Grant
Collaborative Research: Constructing New Multiple Testing Methods
协作研究:构建新的多重测试方法
- 批准号:
1006344 - 财政年份:2010
- 资助金额:
$ 12.66万 - 项目类别:
Standard Grant
Multiple Testing: Further Development Of Theory And Methodology
多重测试:理论和方法的进一步发展
- 批准号:
0603868 - 财政年份:2006
- 资助金额:
$ 12.66万 - 项目类别:
Standard Grant
New Problems in Multiple Hypotheses Testing
多重假设检验中的新问题
- 批准号:
0306366 - 财政年份:2003
- 资助金额:
$ 12.66万 - 项目类别:
Standard Grant
NSF-CBMS Regional Conference in Mathematical Sciences: New Horizons in Multiple Comparison Procedures August 13-17, 2001
NSF-CBMS 数学科学区域会议:多重比较程序的新视野 2001 年 8 月 13-17 日
- 批准号:
0086140 - 财政年份:2000
- 资助金额:
$ 12.66万 - 项目类别:
Standard Grant
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