报告题目：Bootstrap Model Averaging

报告时间：2024年8月12日上午10：00

报告地点：南湖校区老图书馆四楼会议室

主办单位：数学与统计学院/科研处

报告人：邹国华

报告人简介：邹国华，首都师范大学教授。博士毕业于中国科学院系统科学研究所，“新世纪百千万人才工程”国家级人选、中国科学院“百人计划”入选者、享受国务院政府特殊津贴，获中国科学院优秀研究生指导教师称号。主要从事统计学的理论研究及其在经济金融、生物医学中的应用研究工作，在统计模型选择与平均、抽样调查的设计与分析、决策函数的优良性、疾病与基因的关联分析等方面的研究中取得了一系列重要成果。出版教材2本，发表学术论文140余篇；主持和参加过近30项国家科学基金项目及国家级项目。

摘要：Model averaging has gained significant attention in recent years due to its ability of fusing information from different models. The critical challenge in frequentist model averaging is the choice of weight vector. The bootstrap method, known for its favorable properties, presents a new solution. In this paper, we propose a bootstrap model averaging approach that selects the weights by minimizing a bootstrap criterion. We demonstrate that the resultant estimator is asymptotically optimal in the sense that it achieves the lowest possible squared error loss. Furthermore, we establish the convergence rate of bootstrap weights tending to the theoretically optimal weights. Additionally, we derive the limiting distribution of our proposed model averaging estimator. By simulation studies and empirical applications, we show that our proposed method often has better performance than other commonly used model selection and model averaging methods.