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

拟议的研究解决了科学界日益关注的两个问题。一是数据变得如此便宜和容易收集，以至于很难或不可能重现这些数据，甚至是整个研究。其次，算法常常对偏见进行编码，导致意想不到的后果。所谓的可重复性危机和算法公平性问题都有多种原因，但它们共同的一个原因是，大多数大规模数据的不平等可变性，比如它们的异方差。在现代数据问题中改善这些问题需要两个领域的大规模数据统计：多重测试和大规模估计。这两个领域的进展结合在一起，创造了强大的、有理论支持的统计方法。经济、道德和社会影响影响STEM研究、公共政策和技术，以及娱乐、金融和体育等领域，我们将从这些领域分析本提案中的数据。该项目的广泛影响和广泛应用也为少数族裔学生提供了创作学术作品的机会；加强以本科生为主的研究所（PUI）和西班牙裔服务研究所（HSI）的研究基础设施；培训创新科技大学成员的专业发展，帮助他们消除参与科技创新的障碍；并邀请业界参与学术研究反馈循环。这个项目的重点是开发新的推断程序来估计、选择和排序异方差数据。当需要对异方差数据进行选择和排序时（这通常是大规模数据的情况），可复制性和公平性问题就会同时出现。本提案的目标是通过开发新的排序和选择异方差数据的推理程序来解决估计和显著性的双重问题。这三个目标是：(i)根据数据结果的重要性选择有序的假设，（ii）为大规模数据创建更有效和准确的估计器，（iii）创建排名以解决不平等问题。第一个目标将通过逆转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应大小和变异性；第二个目标，通过发展一种凸优化技术；第三个目标，通过创建一个排序算法来解释受保护的特征。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。参与这些研究活动的本科生和研究生将获得广泛的指导和专业发展机会。该奖项反映了美国国家科学基金会的法定使命，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。

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.