主 讲 人：Yuhong Yang 教授 University of Minnesota
Yuhong Yang (email@example.com) received his Ph.D from Yale in statistics in 1996. He then joined Department of Statistics at Iowa State University and moved to the University of Minnesota in 2004. He has been full professor there since 2007. His research interests include model selection, multi-armed bandit problems, forecasting, high-dimensional data analysis, and machine learning. He has published in journals in several fields, including Annals of Statistics, JASA, JRSSB, Biometrika, IEEE Transaction on Information Theory, Journal of Econometrics, Journal of Approximation Theory, Proceedings of AMS, Journal of Machine Leaning Research, and International Journal of Forecasting. He is a fellow of Institute of Mathematical Statistics.
Forecasting has important applications in many areas. Ever since the pioneering work by Clive Granger, a Nobel Laureate in economics, a number of papers have devoted on the topic of how to combine different forecasts. There are several theoretical, computational and practical issues that are crucial to achieve accurate, robust and widely applicable forecast combinations. In this talk, we will introduce the forecast combination problem and provide insight on computational challenges and ways to attain effective combinations. Part of the work is joint with Gang Cheng, Wei Qian, Craig Rolling and Xiaoqiao Wei.