主 题： Large sample covariance matrices and applications
主办单位：统计研究中心 统计学院 科研处
Jianfeng Yao is a professor of Department Statistics and Actuarial Science at The University of Hong Kong. His current research work is concentrated in the field of the theory of random matrices and their applications to high-dimensional statistics. In collaborations with Prof. Zhidong Bai, he has been working on limit theorems for eigenvalue distributions of large dimensional random matrices, especially Wigner matrices and sample covariance matrices.
His other research interests include
i) Statistics of stochastic processes: nonlinear time series, Markov-switching processes
ii) Understanding and probabilistic modelling of digital images
Now, he is the associate editor of the Random Matrices: Theory and Applications and ESAIM: Proceedings.Moreover, he has published a book called Large Sample Covariance Matrices and High-Dimensional Data Analysis in March 2015.
More details please browse the web http://web.hku.hk/~jeffyao/
This talk is a survey on recent results from random matrix theory on large sample covariance matrices. Some key ideas and results meaningful for high-dimensional statistics will be detailed. Significant applications to hypothesis testing on large sample covariance matrix and factor models will be presented.