Reusing Data Efficiently for Iterative and Integrative Inference

从复杂的数据中提取知识和可重现的结果推动了广泛的科学学科。从统计学的角度来看，模型选择和推断是两项基本任务，后者往往是在通过数据驱动程序选择模型之后进行的。天真地为两个任务使用相同的数据会在选定的模型及其推理属性之间产生复杂的相关性，这不可避免地会影响从这些模型得出的结果的重复性。研究人员开发了重复使用选择数据的方法，以补偿这些相关性，同时不会浪费完整数据中的信息。在生物医学问题、行为科学中的观察研究和工程应用中发现立即使用的方法，即使在分析依赖于稀缺样本的情况下，这些方法也将有助于发现。这项研究在创造跨学科参与的机会、培训统计学家和为新的研究生课程做出贡献方面有更广泛的外展部分。该项目旨在通过重复使用模型选择步骤中的数据来进行有效和可重复的推理。该项目结合了凸优化、概率论和统计学习的思想，寻求两个主要问题的解决方案。在第一个推力中，研究人员开发了将稍后可用的新鲜样本与选择的信息相结合的方法。此工作流在现代应用程序中实现，例如在线数据流，这些应用程序需要动态迭代推理。在第二个推力中，研究人员通过组合不同批次或拆分或来源的选定模型来探索综合推理。通过重复使用样本从多个来源聚合推论，将有可能出现任何单个数据集由于缺乏能力而无法报告的新发现。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Drawing knowledge and reproducible results from complex data drives a broad range of scientific disciplines. From a statistical viewpoint, model selection and inference are the two fundamental tasks, the latter often pursued only after models are chosen through data-driven procedures. Naively using the same data for both tasks creates complicated correlations between the selected models and their inferential properties, which inevitably affects the reproducibility of findings from these models. The investigator develops methods for reusing data from selection to compensate for these correlations while not squandering away information from the full data. Finding immediate use in biomedical problems, observational studies in the behavioral sciences, and engineering applications, the methods will aid discoveries even when analyses rely on scarce samples. This research has a broader outreach component in creating opportunities for interdisciplinary engagement, training statisticians, and contributing to a new graduate curriculum.The project is geared towards efficient and reproducible inference through a reuse of data from the model selection steps. Combining ideas from convex optimization, probability theory, and statistical learning, the project seeks solutions for two main thrusts. In the first thrust, the investigator develops methods to integrate fresh samples available at a later point in time with information from selection. This workflow is realized in modern applications such as online streaming of data, which demand iterative inference on the fly. In the second thrust, the investigator explores integrative inference by combining selected models from different batches or splits or sources of data. Aggregating inference from multiple sources through a reuse of samples will have the potential for new discoveries that any single dataset may fail to report due to a lack of power.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.