统计与管理学院2015年学术报告第46期
【主 题】High-Dimensional Dynamic Models for Big Data in Biomedicine and Finance
【报告人】Hulin Wu 教授
Professor and Associate Chair
Department of Biostatistics, School of Public health
Professor, School of Biomedical Informatics
University of Texas Health Science Center at Houston
【时 间】 2015年11月02日（星期一）10:00－11:00
【地 点】 上海财经大学统计与管理学院大楼1208室
【语 言】 英文
【摘 要】Many systems in engineering and physics such as a rocket system can be represented by differential equations, usually in low dimensions, which can be derived from well-established physics laws and theories. However, in many other fields, for examples, a biological system or a financial or economic system is formed by many elements with complex dynamic interplays, no laws or theories exist to deduce exact quantitative relationships and interactions among these elements. It is unclear whether these high-dimensional dynamic systems follow a mathematical representation such as differential equations, similar to that for a man-made physics or engineering system. Fortunately, recent advances in cutting-edge technologies allow us to generate intensive high-throughput data to gain insights into these systems. It is badly needed to develop mathematical models and statistical methods to test whether these systems follow a mathematical representation based on the collected data. In this talk, I will present and discuss how to construct data-driven differential equations (ODE) to describe high-dimensional dynamic systems. We propose to combine the high-dimensional variable selection approaches and ODE model estimation methods to construct the ODE models based on observed data. We apply the proposed approaches to study high-dimensional biological systems and stock market systems based on the time course Big Data. The established systems have dual properties, network properties and dynamic system properties, which can also be used to interpret the features of the high-dimensional dynamic systems.
【邀请人】黄 坚
