CAREER: New data integration approaches for efficient and robust meta-estimation, model fusion and transfer learning

职业：新的数据集成方法，用于高效、稳健的元估计、模型融合和迁移学习

• 批准号: 2337943
• 负责人: Emily Hector
• 金额: $ 45万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-06-01 至 2029-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2337943&HistoricalAwards=false
• 关键词: CAREER; New; data; integration; approaches

Statistical science aims to learn about natural phenomena by drawing generalizable conclusions from an aggregate of similar experimental observations. With the recent “Big Data” and “Open Science” revolutions, scientists have shifted their focus from aggregating individual observations to aggregating massive publicly available datasets. This endeavor is premised on the hope of improving the robustness and generalizability of findings by combining information from multiple datasets. For example, combining data on rare disease outcomes across the United States can paint a more reliable picture than basing conclusions only on a small number of cases in one hospital. Similarly, combining data on disease risk factors across the United States can distinguish local from national health trends. To date, statistical approaches to these data aggregation objectives have been limited to simple settings with limited practical utility. In response to this gap, this project develops new methods for aggregating information from multiple datasets in three distinct data integration problems grounded in scientific practice. The developed approaches are intuitive, principled and robust to substantial differences between datasets, and are broadly applicable in medical, economic and social sciences, among others. Among other applications, the project will deliver new tools to extract health insights from large electronic health records databases. The project will support undergraduate and graduate student training, course development, and the recruitment and professional mentoring of under-represented minorities in statistics. Further, the project will impact STEM education through a data science teacher training program in underserved communities.This project develops intuitive, principled, robust and efficient methods in three essential data integration problems: meta-analysis, model fusion and transfer learning. First, the project delivers a set of meta-analysis methods for privacy-preserving one-shot estimation and inference using a new notion of dataset similarity. The primary novelty in the approach is the joint estimation of both dataset-specific parameters and a combined parameter that bears some similarity to the classic meta-estimator. Second, the project establishes model fusion methods that learn the clustering of similar datasets. The methods’ unique feature is a model fusion that dials data integration along a spectrum of more to less fusion and thereby does not force model parameters from clustered datasets to be exactly equal. Third, the project develops flexible and robust transfer learning approaches that leverage historical information for improved statistical efficiency in a target dataset of interest. An important element of these approaches is a flexible specification of the type of models fit to the source datasets. All three sets of methods place a premium on interpretability, statistical efficiency and robustness of the inferential output. The project unifies the three sets of proposed methods under a formal data integration framework formulated around two axioms of data integration. Data integration ideas pervade every field of scientific study in which data are collected, and so the research contributes to scientific endeavors in the medical, economic and social sciences, among others.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.

通过与最近的“开放科学”相似的经验得出可概括的结论来了解自然现象。通过将多个数据集的信息结合起来，结合了整个美国的稀有疾病结果的数据，比仅仅在一个医院中仅组合疾病风险因素的数据，就可以绘制更可靠的情况。美国可以将本地趋势与日期区分开来。在实践中。在统计数据中，该项目将影响教师老师老师的茶点。使用新的数据集概念的荟萃分析方法的一声估计和推理。估算器。第三，帽子在感兴趣的目标数据集中利用了效率的效率。数据整合。数据整合遍布每个科学研究的领域，其中数据是医学，经济和社会科学领域的收藏家，这是反映NSF'SF'STUTUTORY MISSION基金会的智力优点和更广泛的影响审查标准。