报告人：王晨
报告地点：腾讯会议ID:632 642 069
报告时间：2021年12月2日星期四10:00-11:00
报告摘要：
In this paper, we analyse singular values of a large p×n data matrix Xn=(xn1,…,xnn) where the column xnj's are independent p-dimensional vectors, possibly with different distributions. Such data matrices are common in high-dimensional statistics. Under a key assumption that the covariance matrices Σnj=Cov(xnj) can be asymptotically simultaneously diagonalizable, and appropriate convergence of their spectra, we establish a limiting distribution for the singular values of Xn when both dimension p and n grow to infinity in a comparable magnitude. The matrix model goes beyond and includes many existing works on different types of sample covariance matrices, including the weighted sample covariance matrix, the Gram matrix model and the sample covariance matrix of linear time series models.
主讲人简介：
王晨，毕业于新加坡国立大学，现任香港大学统计及精算系助理教授，研究方向为随机矩阵，时间序列，高维数据分析，在Econometrica, Journal of Econometrics, Annals of Applied Probability等期刊上发表多篇论文。
