报告题目：Corrected Mallows Model Averaging Approach

报告时间：2018年1月4日（周四） 16:00

报告地点：统计学院办公楼1层104

主讲人：邹国华，首都师范大学特聘教授。博士后就读于北京师范大学。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、决策函数的优良性、抽样调查的设计与分析、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1本，在《中国科学》、Biometrika、Genetics、Journal of Econometrics、Journal of the American Statistical Association 等国内外著名期刊上发表论文百余篇；主持或参加过二十余项国家自然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。

报告摘要：An important problem with model averaging approach is the choice of weights. The Mallows criterion for choosing weights suggested by Hansen (2007) is the first asymptotically optimal criterion, which has been used widely. In this paper, we propose a corrected Mallows model averaging (MMAc) method based on small sample F distribution. MMAc exhibits the same asymptotic optimality as Mallows model averaging (MMA) in the sense of minimizing the squared errors in large sample sizes. The consistency of the MMAc based weights tending to the optimal weights minimizing MSE is also studied. We derive the convergence rate of the new empirical weights. Similar property for MMA and Jackknife model averaging (JMA) by Hansen & Racine (2012) is established as well. An extensive simulation study shows that MMAc often performs better than MMA and other commonly used model averaging methods, especially for small and moderate sample size cases. The results from two real data analyses also support the proposed method.