报 告 人：邹国华，首都师范大学教授

报告时间：2025.07.16  14:00-15:00

报告地点：九游在线本部天元讲堂

报告摘要：In the age of big data, model averaging has been proved to be a powerful tool for data analysis, which helps to mitigate bias and reduce over?tting that can arise from relying on a single model. However, outliers in large-scale datasets like image recognition and fraud detection can severely degrade traditional model averaging built on least squares or maximum likelihood. To address this challenge, we propose a robust jackknife model averaging (RJMA) approach, where the weights are selected by minimizing a leave-one-out cross-validation criterion. This framework is adaptable to situations where the dimensions of candidate models increase with the sample size. We establish the asymptotic optimality of the RJMA estimator, demonstrating its ability to minimize out-of-sample ?nal prediction errors. We also present the consistency of the proposed weight estimator to the theoretically optimal weight vector. Furthermore, in scenario where one or more correct models are present in the candidate model set, we show that RJMA assigns all weights to the correct models, leading to a consistent model averaging estimator. Additionally, we derive the in?uence function of the RJMA estimator and introduce the empirical prediction in?uence function to quantitatively evaluate its robustness. To illustrate the ef?cacy of our proposed methodology, we conduct numerical studies including Monte Carlo simulations and a real data analysis, which con?rm the practical applicability and robustness of the RJMA approach.

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，是国家杰出青年基金获得者、“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，得到了国内外同行的好评与肯定，并被广泛引用。共出版教材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以及全国性的实际课题，提出的预测方法被实际部门所采用。