报告题目：Prediction using many samples with models containing partially shared parameters
报 告 人：张新雨 中科院系统所
When a model of main research interest shares partial parameters with several other models, it is of bene_t 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.
张新雨，中科院系统所/预测中心研究员，Texas A&M大学博士后、Penn State 大学Research Fellow。主要研究方向为模型平均、模型选择、组合预测等。国家杰出青年科学基金获得者，主持3项国家自然科学基金，目前担任《JSSC》、《SADM》、《系统科学与数学》、《应用概率统计》编委和《Econometrics》客座主编。