时 间：2025年3月26日 16:00 - 17:00
报告人：戴国榕 复旦大学讲师
地 点：普陀校区理科大楼A1514
主持人：马慧娟 华东师范大学副教授
摘 要：
We consider a general statistical estimation problem involving a finite-dimensional target parameter vector. Beyond an internal data set drawn from the population distribution, external information, such as additional individual data or summary statistics, can potentially improve the estimation when incorporated via appropriate data fusion techniques. However, since acquiring external information often incurs costs, it is desirable to assess its utility beforehand using only the internal data. To address this need, we introduce a utility measure based on estimation efficiency, defined as the ratio of semiparametric efficiency bounds for estimating the target parameters with versus without incorporating the external information. It quantifies the maximum potential efficiency improvement offered by the external information, independent of specific estimation methods. To enable inference on this measure before acquiring the external information, we propose a general approach for constructing its estimators using only the internal data, adopting the efficient influence function methodology. Several concrete examples, where the target parameters and external information take various forms, are explored, demonstrating the versatility of our general framework. For each example, we construct point and interval estimators for the proposed measure and establish their asymptotic properties. Simulation studies confirm the finite-sample performance of our approach, while a real data application highlights its practical value. In scientific research and business applications, our framework significantly empowers cost-effective decision making regarding acquisition of external information.
报告人简介：
戴国榕，复旦大学管理学院统计与数据科学系讲师。他于2019年获Texas A&M统计学博士学位，随后留校从事博士后研究工作，直至2021年加入复旦大学。戴国榕博士的研究兴趣包括高维统计、缺失数据、半参数理论、变量重要性，以及统计方法在生物医学中的应用；其论文发表于Biometrics, JRSSB, Statistica Sinica等期刊。