Factor model for high dimensional matrix valued time series, with possible extension to tensor time series
报告人： Rong Chen, Rutgers University
时间：2017-09-21 15:10 ~ 16:10
n finance, economics and many other field, observations in a matrix form and tensor form are often observed over time. For example, many economic indicators are obtained in different countries over time. Various financial characteristics of many companies over time. Although it is natural to turn the matrix observations into a long vector then use standard vector time series models or factor analysis, it is often the case that the columns and rows of a matrix represent different sets of information that are closely interplayed. We propose a novel factor model that maintains and utilizes the matrix structure to achieve greater dimensional reduction as well as easier interpretable factor structure. Estimation procedure and its theoretical properties and model validation procedures are investigated and demonstrated with simulated and real examples. Extension to tensor time series will be discussed.
About the Speaker:
Rong Chen is Distinguished Professor at Department of Statistics and Biostatistics, Rutgers University. He also serves as Director of Master in Financial Statistics and Risk Management Program and Director of Master in Data Science (MSDS) Statistics Track at Rutgers University. His research areas include Nonlinear and Multivariate Time Series Analysis, Monte Carlo Methods, Statistical Computing and Bayesian Analysis, Statistical Applications in Science, Engineering and Business. He is elected member of International Statistical Institute, and elected fellow of Institute of Mathematical Statistics and American Statistical Association. He has also served as Program Director of Division of Mathematical Sciences, USA National Science Foundation, and Chair of Department of Business Statistics and Econometrics, Peking University.