Abstract

With the rapid development of sensing and data storage technology, various types of high-dimensional data such as financial data, genetic data, complex production process data, medical and health data, are often encountered in practical applications.  The biggest challenge in analyzing high-dimensional data is the “curse of dimension”. As the dimension increases, the complexity of analyzing and processing high-dimensional data grows exponentially. Too many covariates make it impossible to use many traditional statistical methods effectively. This talk discusses some applications of modern high-dimensional statistics to sparse index tracking problems.

About the Speaker

Dr. Lianjie Shu is currently Professor in Faculty of Business Administration at University of Macau. He received his Bachelor degree in Mechanical Engineering and Automation from Xi'an Jiao Tong University in 1998, and his Ph.D. in Industrial Engineering and Engineering Management from the Hong Kong University of Science and Technology (HKUST) in 2002, respectively. He currently serves an Associate Editor on Journal of Statistical Computation and Simulation and a Senior Editor on Journal of Industrial and production Engineering. He is a senior member of Institute of Industrial and Systems Engineers (IISE) and American Society for Quality (ASQ). His recent research interests include Quantitative Finance, High-dimensional Statistics, and Statistical Quality Control. His publications appear on a wide variety of journals such as Journal of Financial and Quantitative Analysis, Statistics in Medicine, Naval Research & Logistics, IISE Transactions, Journal of Quality Technology, etc.

Poster