Time: 9:00-10:30 on Sep. 13th
Place: Lecture Hall on Floor 1 in Teaching Building 3, Yanshan Campus
Hosted by: School of Statistics
Abstract: Currently, it is strongly needed to develop both fundamental theories and applied methods for analyzing big-data. Furthermore, efficient algorithms are also needed for applications in internet financial risk management, market behavior and decisions under high frequency trades, and so on. In this proposal, our research focuses on statistical inferences and computing algorithms based on dimension reduction of high-dimensional data. We develop new methods based on divide-conquer for data set with huge sample size. Statistical inferences and computing algorithms based on dimension reduction of high-dimensional financial data are proposed. These topics emphasize on models, methods and computation for financial big-data analysis.