New Methodology for Multiple Testing and Simultaneous Inference

多重测试和同时推理的新方法

• 批准号: 0707085
• 负责人: Joseph Romano
• 金额: $ 26.24万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-07-01 至 2011-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0707085&HistoricalAwards=false
• 关键词: New; Methodology; Multiple; Testing; Simultaneous

The investigator develops new methods and theory for problems in multiple testing and simultaneous inference. A classical approach to dealing with multiplicity is to require that decision rules control the familywise error rate. But, when the number of tests is large, this measure is so stringent that alternative hypotheses have little chance of being detected. Thus, alternative measures of error control are studied both in finite sample and asymptotically. Such measures include: the false discovery rate; the probability of k or more false rejections; tail probabilities of the false discovery proportion. In order to develop methods which do not rely on unrealistic or unverifiable model assumptions, the investigator makes extensive use of computer-intensive methods. The power of resampling is that the joint dependence structure of the individual tests can be captured so that methods need not be overly conservative. The pursuit of such methodology is investigated from theoretical, computational and theoretical points of view, with special emphasis on a large number of tests.The goal of this research is to develop new theory and methods for problems of multiple inference. Virtually any scientific experiment sets out to answer questions about the process under investigation, which often can be translated formally into a set of hypotheses to be tested. It is the exception that only a single hypothesis or question is under study. In the "information age", the statistician is faced with the challenge of accounting for all possible errors resulting from a complex data analysis, so that any interesting conclusions can reliably be viewed as real structure rather than the result of "data snooping", i.e. finding artifacts of random data. For example, current methods in biotechnology and genomics generate DNA microarray experiments, where gene expression level in cells for thousands of genes are analyzed simultaneously on a gene by gene basis. The goal then is to devise new techniques that are not based on strong assumptions that effectively deal with problems of multiplicity in the face of vast amounts of data. The resulting inferential tools can be applied to such diverse fields as genetics, econometrics, finance, brain imaging, clinical trials, education and astronomy.

研究者为多重测试和同时推理中的问题发展了新的方法和理论。处理多重性的一种经典方法是要求决策规则控制家庭错误率。但是，当测试数量很大时，这一措施是如此严格，以至于替代假设几乎没有机会被发现。因此，在有限样本和渐近样本下都研究了误差控制的替代措施。这些度量包括：错误发现率；k个或更多错误拒绝的概率；错误发现比例的尾概率。为了开发不依赖于不切实际或不可验证的模型假设的方法，研究人员广泛使用计算机密集型方法。重采样的强大之处在于，可以捕获单个测试的联合依赖结构，因此方法不需要过于保守。本研究从理论、计算和理论三个角度对多元推理的方法论追求进行了研究，重点是大量的测试，旨在为多元推理问题发展新的理论和方法。实际上，任何科学实验都会回答与研究过程有关的问题，这些问题通常可以正式转化为一系列需要检验的假设。只有一个假设或问题在研究中，这是个例外。在“信息时代”，统计学家面临着一项挑战，即如何解释复杂数据分析产生的所有可能的错误，以便任何有趣的结论都能可靠地被视为真实的结构，而不是“数据窥探”的结果，即寻找随机数据的伪迹。例如，目前生物技术和基因组学中的方法产生了DNA微阵列实验，其中数千个基因在细胞中的基因表达水平是在一个基因一个基因的基础上同时进行分析的。然后，目标是设计新的技术，这些技术不是基于强有力的假设，而是在面对大量数据时有效地处理多样性问题。由此产生的推理工具可以应用于遗传学、计量经济学、金融、脑成像、临床试验、教育和天文学等不同领域。