报告题目：Asymptotic distribution theory for model averaging based on information criterion
报 告 人：邹国华 教授 首都师范大学
报告时间：2022年07月20日 10：00-11：00
报告地点：腾讯会议 ID：587-319-559
会议链接：https://meeting.tencent.com/dm/W6F3io7PiLLU
校内联系人：韩月才 hanyc@jlu.edu.cn
报告摘要：Smoothed AIC (S-AIC) and Smoothed BIC (S-BIC) are widely used in model averaging. Unlike most other model averaging methods, which depend on the specific model structures, S-AIC and S-BIC can be applied to all situations where AIC and BIC can be calculated, and so are very convenient to implement. However, their theoretical property has not been explored yet. In this paper, we aim to fill this gap. We will study the asymptotic behavior of the S-AIC and S-BIC estimators. A significant difference from the literature, where the local misspecification assumption is usually needed, is that we derive the limiting distributions of the S-AIC and S-BIC weights and estimators under framework of fixed parameter. Based on our limiting theory, we investigate the confidence intervals constructed by Burnham and Anderson (2002), who claimed but did not prove that the resultant coverage probabilities would be close to the intended level. Further, under fixed parameter setup, we provide the confidence interval construction methods for the parameters of interest. Both simulation study and real data analysis support our theoretical conclusions.
报告人简介：邹国华，首都师范大学特聘教授。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，曾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120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。
