报告人简介：姚琦伟教授是国际知名的统计学家，现任英国伦敦政治经济学院统计系教授，美国统计协会fellow，国际数理统计学会fellow，英国皇家统计学会fellow，国际统计学会当选会员elected member。主要研究领域为：复杂时间序列分析、时空过程、金融计量经济学。迄今已发表高水平学术论文百余篇，并获得英国国家基金会支持的多项研究基金项目。现任统计学顶级期刊《Journal of the Royal Statistical Society (Series B)》联合主编，已担任包括Annals of Statistics、Journal of the American Statistics Association等多个顶级学术期刊副主编。

摘要：We consider modelling 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 significantly improves 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.

 

(Joint work with Jinyuan Chang, Jing He and Lin Yang)