珠海数学杰出学者讲座第41讲-An Integrated GMM Shrinkage Approach with Consistent Moment Selection from Multiple External Sources

讲座题目：An Integrated GMM Shrinkage Approach with Consistent Moment Selection from Multiple External Sources
讲座时间 Datetime: 2024年12月23日，周一，10:30-11:30
地点 Venue: 海琴二号457
报告人Speaker: 邵军 教授
单位 Affiliation: 威斯康星大学麦迪逊分校
主持人 Host：叶东曦 副教授
报告摘要 Abstract:
Interest has grown in analyzing primary internal data by utilizing some independent external aggregated statistics for efficiency gain. However, when population heterogeneity exists, inappropriate incorporation may lead to a biased estimator. With multiple external sources under generalized estimation equations and possibly heterogeneous populations, we propose an integrated generalized moment method that can perform a data-driven selection of valid moment equations from external sources and make efficient parameter estimation simultaneously. Moment equation selection consistency and asymptotic normality are established for the proposed estimator. Further, when the sample sizes of all external sources are large compared to the internal sample size, asymptotically the proposed estimator is more efficient than the estimator based on the internal data only and is oracle-efficient in the sense that it is as efficient as the oracle estimator based on all valid moment equations. Simulation studies confirm the theoretical results and the efficiency of the proposed method empirically. An example is also included for illustration.
报告人简介：
美国威斯康星大学麦迪逊分校统计学教授，美国统计协会研究员; 1987年获威斯康星大学麦迪逊分校统计学博士学位，1996至今年担任威斯康星大学麦迪逊分校统计系教授；主要研究方向为jackknife、bootstrap 和其他重采样方法，高维数据变量选择和推理，抽样调查（方差估计、非受访者的插补），缺失数据（不可忽略的缺失、dropout 和半参数方法），医学统计（临床试验、个性化医疗、协变量自适应设计）；曾担任Journal of Nonparametric Statistics 、Journal of System Science and Complexity、Journal of American Statistical Association的编委。