Random Matrix Theory and Large Sample Covariance Matrix
主 题: Random Matrix Theory and Large Sample Covariance Matrix
报告人: 王成 (上海交通大学)
时 间: 2017-04-13 14:00-15:00
地 点: 理科1号楼1114
Abstract: In this talk, I will introduce the large dimensional setting and review some developments of random matrix theory from the point of statistical view, including limiting spectral distribution and the CLT of the liner spectral statistics. As applications, some results on testing and estimation for the covariance matrix will be presented. Finally, I will discuss the common assumptions and technical tools in RMT.
About the speaker: Cheng Wang is Special Researcher at the Department of Mathematics, Shanghai Jiao Tong University. He obtained his Ph.D. in Statistics from the University of Science and Technology of China in 2013. His research interests include large random matrix theory and its application, multivariate statistical analysis in large dimensional data, and high-dimensional statistical inference.