报告人：张博
报告地点：腾讯会议ID:722212036
报告时间：2021年12月1日星期三9:30-11:30
报告摘要：
We consider the problem of estimating the number of significant components in high-dimensional principal component analysis (PCA). We propose a new penalized approach using the explained variance ratio and the ratio of the adjacent eigenvalues of sample covariance matrices of p variables. We show the consistency of the estimator for different types of data matrices including independent data and some times series data when the dimension p and the sample size n both tend to infinity with their ratio being a positive constant. Hence it works under weaker conditions than some existing approaches such as AIC and BIC. Simulation studies are also conducted to illustrate its performance. It's a joint work with Zhixiang Zhang and Guangming Pan.
主讲人简介：
张博于新加坡南洋理工大学获得博士学位，曾在澳大利亚莫纳什大学从事博士后工作，现任中国科学技术大学统计与金融系特任副教授。主要研究领域包括大维随机矩阵，高维时间序列和复杂网络问题，已有两篇论文发表于Annals of Statistics.
