Cun-Hui Zhang : Factor Models for High-Dimensional Tensor Time Series

Theme: Factor Models for High-Dimensional Tensor Time Series

Lecturer: Professor Cun-Hui Zhang, Rutgers University

Host: Professor LIN Huazhen, School of Statistics

Date: 11:00-12:00, Sep. 29, 2020

Conference ID: Tencent Conference, 289 217 199

Organizers: Center of Statistical Research, School of Statistics and Office of Research Affairs

Introduction to the Lecturer:

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.

Content Summary:

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 prove 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.