报告时间：2019年10月22日 下午15:30-16:30
报告地点：明德主楼1016
报告题目：Multilinear Low-Rank Vector Autoregressive Modeling via Tensor Decomposition
报告摘要：The VAR model involves a large number of parameters so it can suffer from the curse of dimensionality for high-dimensional time series data. The reduced-rank coefficient model can alleviate the problem but the low-rank structure along the time direction for time series models has never been considered. We rearrange the parameters in the VAR model to a tensor form, and propose a multilinear low-rank VAR model via tensor decomposition that effectively exploits the temporal and cross-sectional low-rank structure. Effectiveness of the methods is demonstrated on simulated and real data.
报告人简介：练恒，现任香港城市大学数学系副教授，于2000年在中国科学技术大学获得数学和计算机学士学位，2007年在美国布朗大学获得计算机硕士，经济学硕士和应用数学博士学位。研究方向包括高维数据分析，函数数据分析，机器学习等。在《Journal of the Royal Statistical Society，Series B》、《Journal of the American Statistical Association》等国际期刊上发表高水平学术论文30多篇。