LEAPS-MPS: Importance, Significance, and Fairness in Large-Scale Estimation and Testing of Heteroscedastic Data

这项拟议的研究解决了科学界日益关注的两个问题。其中之一是，数据已经变得如此便宜和容易收集，以至于往往很难甚至不可能复制这些数据或甚至整个研究。第二个问题是，算法往往会对偏差进行编码，从而产生意想不到的后果。所谓的可再现性危机和算法公平性问题都有多个原因，但它们共有的一个原因是大多数大规模数据的不平等可变性，例如它们的异方差。要改善现代数据问题中的这些问题，需要对大规模数据进行两个领域的统计：多重检验和大规模估计。这两个领域的进步被结合在一起，因为它们以前从未创建过强大的、有理论支持的统计方法。经济、道德和社会影响影响到STEM研究、公共政策和技术，以及娱乐、金融和体育，我们在本提案中从这些领域分析数据。该项目的广泛影响和广泛应用还为未被充分代表的少数族裔(URM)学生提供了发表学术作品的机会；加强了以本科生为主的学院(PUI)和西班牙裔服务学院(HSI)的研究基础设施；培训URM成员进行专业发展，以帮助消除他们参与技术创新的障碍；并邀请行业部门进入学术研究反馈循环。该项目专注于开发新的推理程序，用于估计、选择和排序异方差数据。当需要选择异方差数据并对其进行排序时，大规模数据通常会出现可复制性和公平性问题。这项建议的目标是通过开发新的推理程序来对异方差数据进行排序和选择，以解决估计和意义的双重问题。这三个目标是：(I)根据数据结果的重要性选择有序假设，(Ii)为大规模数据创建更有效和更准确的估计器，(Iii)创建排名以解决不平等问题。第一个目标将通过颠倒排序然后选择假设的典型多重假设检验步骤来实现，这将更好地平衡估计的效果大小和可变性；第二个目标将通过开发凸优化技术来实现；第三个目标将通过创建考虑受保护特征的排名算法来实现。将开发新的算法来实现这些方法，并通过Github、Datalore和R包公开共享。将为参与这些研究活动的本科生和研究生提供广泛的指导和职业发展机会。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。

The proposed research addresses two issues of increasing concern in the scientific community. One is that data has become so cheap and easy to collect that it is all too often difficult or impossible to reproduce these data or even entire studies. The second is that algorithms often encode biases with unintended consequences. The so-called reproducibility crisis and algorithmic fairness problems each have multiple causes, but one that they share is grounded in the unequal variability of most large-scale data such as their heteroscedasticity. Ameliorating these issues in modern data problems requires two fields of statistics for large-scale data: multiple testing and large-scale estimation. Advances in both fields are combined as they have not been previously to create powerful, theoretically backed statistical methodology. The economic, moral, and social impacts affect STEM research, public policy, and technology, as well as entertainment, finance, and sports, fields from which we analyze data in this proposal. The broader impacts and wide application of this project also provide underrepresented minority (URM) students with opportunities to produce scholarly work; bolster research infrastructure at a primarily undergraduate institute (PUI) and Hispanic Serving Institute (HSI); train URM members in professional development to help remove barriers to their participation in technological innovation; and invite the industry sector into the academic research feedback loop.This project focuses on developing new inferential procedures for estimating, selecting, and ranking heteroscedastic data. When heteroscedastic data needs to be selected and ranked, as is often the case with large-scale data, both replicability and fairness issues arise together. The goal of this proposal is to resolve the dual issues of estimation and significance by developing new inferential procedures for ranking and selecting heteroscedastic data. The three objectives are to: (i) select ordered hypotheses based on the importance of data results, (ii) create more efficient and accurate estimators for large-scale data, (iii) create rankings to address inequities. The first objective will be accomplished by reversing the typical multiple hypothesis testing steps of ordering and then selecting hypotheses, which will better balance estimated effect size and variability; the second objective, by developing a convex optimization technique; and the third objective, by creating a ranking algorithm that accounts for protected characteristics. New algorithms will be developed to implement each of these methods and publicly shared via Github, DataLore, and R packages. Extensive mentorship and professional development opportunities will be offered to undergraduate and graduate students who participate in these research activities.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.