讲座题目：A GMM Approach in Coupling Internal Data and ExternalSummary Information with Heterogeneous Data Populations

主 讲 人：威斯康星大学麦迪逊分校邵军教授

讲座时间：2023年12月13日（周三）14：00-15：00

讲座地点：6号学院楼402

主办单位：59白菜专区论坛总站、浙江省2011“数据科学与大数据分析协同创新中心”

摘要：

Because of advances in data collection and storage, statistical analysis in modernscientific research and practice now has opportunities to utilize external informationsuch as summary statistics from similar studies. A likelihood approach based on aparametric model assumption has been developed in the literature to utilize externalsummary information when the populations for external data and the main internaldata are assumed to be the same. In this article we instead consider the generalizedestimation equation (GEE) approach for statistical inference, which is semiparametricor nonparametric, and show how to utilize external summary information even wheninternal and external data populations are not the same. Our approach is couplingthe internal data and external summary information to form additional estimationequations, and then applying the generalized method of moments (GMM). We showthat the proposed GMM estimator is asymptotically normal and, under some conditions, is more efficient than the GEE estimator without using external summaryinformation. Estimators of asymptotic covariance matrix of the GMM estimators arealso proposed. Simulation results are obtained to confirm our theory and to quantify the improvements from utilizing external data. An example is also included forillustration.

主讲人简介：

邵军，美国威斯康星大学麦迪逊分校统计系教授，1996年获美国数理统计学会Fellow，1999年获美国统计学会Fellow，多次获得美国自然科学基金，曾担任美国威斯康星大学麦迪逊分校统计系系主任（2005-2009）、泛华统计学会会长（2007），现兼任美国国家统计局高级研究员，并任美国多家制药厂的统计顾问，2009年入选“国家-”，现为华东师范大学特聘教授。邵教授曾任JASA、Statistica Sinica副主编，Journal of Multivariate Analysis和Sankhya联合主编，现任Journal of Nonparametric Statistics主编，Journal of System Science and Complexity联合主编，2017年联合创立Statistical Theory and Related Fields并担任总编辑。邵教授的6本统计学专著和课本之一的《数理统计》已成为数理统计理论名著，并成为北美和中国多个大学的统计学研究生教材。自1987年以来邵教授共发表学术论文180余篇，在重抽样技术、变量选择、生物统计和缺失数据的统计处理等方面做了大量的开创性工作。

欢迎感兴趣的师生积极参加！