报告题目:Estimating the number of significant components in high-dimensional PCA
报告人：潘光明 教授 新加坡南洋理工大学
报告时间：2021年6月14日 9：00至10：00
报告地点：腾讯会议号：906 190 795
校内联系人：丁雪 dingxue83@jlu.edu.cn
报告摘要:We propose an information criteria to estimate the number of significant components in high-dimensional principal component analysis(PCA). The information criteria is based on the ratio of explained variance and eigenvalue ratios. We show consistency of the estimator in general cases by random matrix theory. We compare its performance with AIC, BIC and some other existing methods for estimating the number of significant components in terms of both theoretical aspects and simulations. An example about the stocks in S&P500 is also reported.
报告人简介: 潘光明，新加坡南洋理工大学教授。2005年7月博士毕业于中国科学技术大学，自2008年以来，在新加坡南洋理工大学工作。研究领域包括高维统计推断、随机矩阵理论、多元统计、应用概率等，至今在统计学和概率论的顶级杂志，如: Annals of Statistics, Journal of American Statistical Association, Journal of Royal Statistical Society（B）, 《Annals of Probability》、《Annals of Applied Probability》、《Bernoulli》等上发表论文50余篇。现为国际统计学会会员(Elected Member of International Statistical Institute)。担任《Random Matrices: Theory and Applications》杂志编委。
