题目：Simultaneous Decorrelation of Matrix Time Series

主讲人：伦敦政治经济学院 姚琦伟教授

主持人：西南财经大学统计学院 常晋源教授

时间：2024年7月18日（周四）上午09:00-12:00

          2024年7月19日（周五）上午09:00-12:00

          2024年7月20日（周六）上午09:00-12:00

    2024年7月21日（周日）上午09:00-12:00

地点：西南财经大学光华校区光华楼1003会议室

报告摘要：

We propose a contemporaneous bilinear transformation for a p×q matrix time series to alleviate the difficulties in modeling and forecasting matrix time series when p and/or q are large. The resulting transformed matrix assumes a block structure consisting of several small matrices, and those small matrix series are uncorrelated across all times. Hence an overall parsimonious model is achieved by modelling each of those small matrix series separately without the loss of information on the linear dynamics. Such a parsimonious model often has better forecasting performance, even when the underlying true dynamics deviates from the assumed uncorrelated block structure after transformation. The uniform convergence rates of the estimated transformation are derived, which vindicate an important virtue of the proposed bilinear transformation, i.e. it is technically equivalent to the decorrelation of a vector time series of dimension max (p,q)  instead of p×q. The proposed method is illustrated numerically via both simulated and real data examples.

主讲人简介：

姚琦伟教授，英国伦敦政治经济学院统计系教授，英国皇家统计学会会士，美国统计协会会士，数理统计学会会士，国际统计研究学会选举会员，泛华统计学会会员。主要研究领域为时间序列分析、高维时间序列建模和预测、降维和因子建模等。迄今已在AoS、Biometrika、Econometrica、JoE、JASA和JRSSB等期刊上发表学术论文90多篇，并获得EPSRC、BBSRC等英国国家基金会支持的多项研究基金项目。目前担任JRSSB的联合主编，曾担任AoS、JBES、JASA和Statistica Sinica等期刊的副主编和联合主编。