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罗格斯大学Prof.Cun-Hui Zhang:Factor Models for High-Dimensional Tensor Time Series

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主题 Factor Models for High-Dimensional Tensor Time Series

主讲人罗格斯大学Prof.Cun-Hui Zhang

主持人统计学院林华珍教授

时间2020年9月29日(周二)上午11:00-12:00

直播平台及会议ID腾讯会议,289 217 199

主办单位:统计研究中心和统计学院 科研处

主讲人简介:

          Cun-Hui Zhang, Distinguished Professor of Statistics at Rutgers University, is a Fellow of the Institute of Mathematical Statistics and a Fellow of American Statistical Association. His research interests include high-dimensional data, machine learning, empirical Bayes, time series, nonparametric methods, multivariate analysis, survival data and biostatistics, functional MRI, closed loop diabetes control, and network tomography.

      Prof. Cun-Hui Zhang, 是罗格斯大学统计系特聘教授,是IMS和ASA的Fellow。他的研究兴趣包括:高维数据,机器学习,经验贝叶斯,时间序列,非参数方法,多元分析,生存数据和生物统计学,功能性MRI,闭环糖尿病控制和网络断层扫描。他共发表了论文130余篇(其中在统计学顶级期刊AoS、JASA、Biometrika、JRSSB上发表论文近40篇,在PNAS、JMLR、NIPS、KDD上发表论文近20篇)。

详情请见其个人主页:http://stat.rutgers.edu/home/cunhui/

内容提要:

    Large tensor data are now routinely collected in a wide range of applications due to rapid development of information technologies and their broad implementation in our era. Often such observations are taken over time, forming tensor time series. We present a factor model approach for analyzing high-dimensional dynamic tensor time series and multi-category dynamic transport networks. Two estimation procedures are developed along with iterative projection algorithms to improve them. Theoretical results provide guaranteed convergence rates and proves the benefit of the iterative projections. Simulation results support the theory. Real applications are used to illustrate the model and its interpretations. This is joint work with Rong Chen, Yuefeng Han and Dan Yang.

由于信息技术的飞速发展及其在当今时代的广泛实施,大张量数据现在已在各种应用中常规收集。 通常,这种观察是随时间变化的,形成张量时间序列。 我们提出了一种因子模型方法来分析高维动态张量时间序列和多类别动态传输网络。 开发了两种估计程序以及迭代投影算法来改进它们。 理论结果提供了有保证的收敛速度,并证明了迭代预测的好处。 仿真结果支持了这一理论。 实际应用程序用于说明模型及其解释。 这是与Rong Chen, Yuefeng Han and Dan Yang共同完成的。


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