主题: 连接数据的孤岛---基于模型平均方法

Abstract: 

We consider prediction based on a main model. When themain model shares partial parameters with several other helper models, we make use of the additional information. Specifically, we propose a Model Averaging Prediction (MAP) procedure that takes into account data related to the main model as well as data related to the helper models. We allow the data related to different models to follow different structures, as long as they share some common covariate effect. We show that when the main model is misspecified, MAP yields the optimal weights in terms of prediction. Further, if the main model is correctly specified, then MAP will automatically exclude all incorrect helper models asymptotically. Simulation studies are conducted to demonstrate the superior performance of MAP. We further implement MAP to analyze a dataset related to the probability of credit card default.

报告时间：2023年9月4日下午16:00

线下地点：威廉希尔6-210会议室

腾讯会议ID: 116-350-278

嘉宾简介：

张新雨，中科院数学与系统科学研究院预测中心研究员。主要从事计量经济学和统计学的理论和应用研究工作，具体研究方向包括模型平均、机器学习和组合预测等，发表论文80余篇，其中多篇论文发表在统计学四大期刊和计量经济学顶级期刊JoE。担任SCI期刊《JSSC》领域主编和其他5个国内外重要期刊的编委，是双法学会数据科学分会副理事长、国际统计学会当选会员，先后主持自科优秀和杰出青年基金项目，曾获中国青年科技奖。