時   間：2025年3月26日 16:00 - 17:00

報告人：戴國榕   復(fù)旦大學(xué)講師

地   點(diǎn)：普陀校區(qū)理科大樓A1514

主持人：馬慧娟   華東師范大學(xué)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.

報告人簡介：

戴國榕，復(fù)旦大學(xué)管理學(xué)院統(tǒng)計與數(shù)據(jù)科學(xué)系講師。他于2019年獲Texas A&M統(tǒng)計學(xué)博士學(xué)位，隨后留校從事博士后研究工作，直至2021年加入復(fù)旦大學(xué)。戴國榕博士的研究興趣包括高維統(tǒng)計、缺失數(shù)據(jù)、半?yún)?shù)理論、變量重要性，以及統(tǒng)計方法在生物醫(yī)學(xué)中的應(yīng)用；其論文發(fā)表于Biometrics, JRSSB, Statistica Sinica等期刊。