报告人：常晋源(西南财经大学光华特聘教授)
主持人：李仲飞
讲座时间：2022年12月22日（周四）下午14:30-16:30
腾讯会议：903-120-775
讲座介绍：
      We consider to model matrix time series based on a tensor CP-decomposition. Instead of using an iterative algorithm which is the standard practice for estimating CP-decompositions, we propose a new and one-pass estimation procedure based on a generalized eigenanalysis constructed from the serial dependence structure of the underlying process. To overcome the intricacy of solving a rank-reduced generalized eigenequation, we propose a further refined approach which projects it into a lower-dimensional full-ranked eigenequation. This refined method improves significantly the finite-sample performance of the estimation. The asymptotic theory has been established under a general setting without the stationarity. It shows, for example, that all the component coefficient vectors in the CP-decomposition are estimated consistently with certain convergence rates. The proposed model and the estimation method are also illustrated with both simulated and real data; showing effective dimension-reduction in modelling and forecasting matrix time series
报告人介绍：
      常晋源，西南财经大学光华特聘教授、博士生导师、数据科学与商业智能联合实验室执行主任、国家杰出青年科学基金获得者、四川省特聘专家、四川省统计专家咨询委员会委员。主要从事“超高维数据分析”和“高频金融数据分析”两个领域的研究。