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

LEAPS-MPS：异方差数据大规模估计和测试的重要性、意义和公平性

• 批准号: 2316746
• 负责人: Luella Fu
• 金额: $ 25万
• 依托单位: San Francisco State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2316746&HistoricalAwards=false
• 关键词: LEAPS; MPS; Importance; Significance; Fairness

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.

拟议的研究解决了科学界日益关注的两个问题。一个是数据已经变得如此便宜和容易收集，以至于复制这些数据甚至整个研究往往是困难或不可能的。第二个是算法经常对偏差进行编码，并产生意想不到的后果。所谓的再现性危机和算法公平性问题都有多种原因，但它们共同的原因是大多数大规模数据的不平等变异性，例如它们的异方差性。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：多重检验和大规模估计。这两个领域的进步结合起来，因为它们以前没有创造强大的，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，我们在本提案中分析数据的领域。该项目的广泛影响和广泛应用还为代表性不足的少数民族学生提供了从事学术工作的机会;加强了主要是本科生的研究所和西班牙裔服务研究所的研究基础设施;培训少数民族成员的专业发展，以帮助消除他们参与技术创新的障碍;并邀请产业界加入学术研究的回馈循环。2这个计画的重点是发展新的推论程序来估计、选择和排列异方差资料。当需要选择异方差数据并对其进行排序时，通常是大规模数据的情况，可复制性和公平性问题同时出现。这个建议的目标是解决估计和重要性的双重问题，通过开发新的推理程序，排名和选择异方差数据。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准确的估计器，（iii）创建排名以解决不公平问题。第一个目标将通过颠倒排序和选择假设的典型多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;第二个目标通过开发凸优化技术来实现;第三个目标通过创建考虑受保护特征的排名算法来实现。我们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。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。