报告题目：Prediction using many samples with models containing partially shared parameters

时 间： 2020年9月20日（星期日）上午11:00

地 点：翡翠湖校区翡翠科教楼B座1710

报告人：张新雨 研究员

工作单位: 中国科学院数学与系统科学研究院/中国科学院预测科学研究中心

举办单位：华体会网页入口

报告人简介：张新雨，中国科学院数学与系统科学研究院/中国科学院预测科学研究中心研究员，2010年在中科院系统所获博士学位，智源青年科学家；曾在德州农工大学做博士后研究。主要从事统计学和计量经济学的理论和应用研究工作,研究兴趣包括模型平均、机器学习、组合预测和卫生统计等。发表了50多篇学术论文,其中20余篇论文发表在Annals of Statistics、Biometrika、JASA、JRSSB、Journal of Econometrics和Econometric Theory。担任期刊《Journal of Systems Science and Complexity》领域主编、期刊《Statistical Analysis and Data Mining》AE、以及期刊《系统科学与数学》、《应用概率统计》的编委，是国际统计学会当选会员。先后主持国家自然科学基金委优秀青年基金和杰出青年基金项目，曾获得中国管理学青年奖和中科院优秀博士学位论文等奖励。

报告摘要：When a model of main research interest shares partial parameters with several other models, it is of benet to incorporate the information contained in these other models to improve the estimation and prediction for the main model of interest. Various methods are possible to make use of the additional models as well as the additional observations related to these models. We propose an optimal strategy of doing so in terms of prediction. We develop the model averaging methodology and obtain the optimal weights. We also establish theory to support the method and show its desirable properties both when the main model is correct and when it is incorrect. Numerical experiments including simulation studies and data analysis are conducted to demonstrate the superior performance of our methods