报告人：常晋源
报告地点：腾讯会议ID:766 135 991
报告时间：2022年07月15日星期五10:00-11:00
报告摘要：
We propose 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. A key idea of the new procedure is to project a generalized eigenequation defined in terms of rank-reduced matrices to a lower-dimensional one with full-ranked matrices, to avoid the intricacy of the former of which the number of eigenvalues can be zero, finite and infinity. 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 the different error rates, depending on the relative sizes between the dimensions of time series and the sample size. The proposed model and the estimation method are further illustrated with both simulated and real data; showing effective dimension-reduction in modelling and forecasting matrix time series. (A joint work with Jing He, Lin Yang and Qiwei Yao)
主讲人简介：
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。
