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Interactive Methods for Data-Adaptive Multiple Testing

数据自适应多重测试的交互式方法

基本信息

批准号：
1916220
负责人：
William Fithian
金额：
$ 18万
依托单位：
University of California-Berkeley
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-01 至 2022-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1916220&HistoricalAwards=false
关键词：
Interactive Methods Data Adaptive Multiple

项目摘要

In modern applications, very large and complex data sets are routinely collected without a specific research question in mind. Rather, the express goal is to explore the data in search of novel insights, discovering relationships and structure they may not expected to be found, then report inferences for those findings. This project provides a series of iterative algorithmic frameworks for interactive multiple testing, which blend exploratory and confirmatory analyses. The frameworks are adaptive in an unusually strong sense: at each step of the procedure, the algorithm reveals more data to the analyst who makes data-driven decisions that guide the procedure.The starting point of this project is the PI's recent work on the AdaPT and STAR algorithms, which give flexible and powerful frameworks for controlling the FDR while exploiting side information (AdaPT) or enforcing constraints on the rejection set (STAR). Given predictor for each p-value, AdaPT lets analysts interactively estimate Bayes-optimal p-value weights from the data using any machine learning method, while provably controlling finite-sample FDR. The STAR likewise allows for generic interactive modeling while also guaranteeing that the rejection set satisfies natural structural constraints such as hierarchy principles in regression. Both methods guarantee FDR control by means of optional stopping arguments, giving near-total flexibility to the analyst to adaptively model the data. The PI will work on four projects that continue this research program by developing new interactive methods and applying AdaPT to current scientific problems.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在现代应用程序中，非常大和复杂的数据集被定期收集，而没有考虑到特定的研究问题。相反，明确的目标是探索数据以寻找新的见解，发现它们可能没有被发现的关系和结构，然后报告这些发现的推论。该项目提供了一系列交互式多重测试的迭代算法框架，其中混合了探索性和验证性分析。这些框架在一个非常强烈的意义上是适应性的：在程序的每一步，该算法向分析师展示更多的数据，分析师做出数据驱动的决策来指导程序。该项目的起点是PI最近在AdaPT和星星算法上的工作，它提供了灵活而强大的框架，用于控制FDR，同时利用边信息（AdaPT）或对拒绝集强制约束（星星）。给定每个p值的预测器，AdaPT允许分析师使用任何机器学习方法从数据中交互式估计贝叶斯最优p值权重，同时可证明地控制有限样本FDR。星星同样允许通用的交互式建模，同时也保证拒绝集满足自然的结构约束，如回归中的层次原则。这两种方法都通过可选的停止参数来保证FDR控制，为分析人员提供了几乎完全的灵活性，以自适应地对数据进行建模。PI将致力于四个项目，通过开发新的交互方法和将AdaPT应用于当前的科学问题来继续这一研究计划。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。