【专家简介】：潘光明，新加坡南洋理工大学教授，博士生导师。2002年硕士毕业于安徽大学，2005年博士毕业于中国科学技术大学，之后在新加坡国立大学、台湾国立中山大学、荷兰埃因霍温科技大学做博士后和学术交流工作;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。至今已在Annals ofStatistics、Journal of American Statistical Association、Journal ofRoyal Statistical Society ( B)、Annals of Probability、Annals ofApplied Probability、Bernoulli、IEEE Transactions on Signal Pro-cessing、IEEE Transactions on Information Theory等顶级统计学杂志上发表60余篇学术论文。现为国际统计学会会员(Elected Member ofnternational Statistical Institute)，《Random Matrices: Theory andApplications》杂志编委。
【报告摘要】：We consider the problem of estimating the number of significant components in highdimensional principal component analysis (PCA). We propose a new penalized approach using the explained variance ratio and the rigidity of the nonspiked sample eigenvalues of sample covariance matrices of p variables. Compared with the existing literature, the consistency of the estimator holds not only for independent data but also some times series data when the dimension p and the sample size n both tend to infinity. Even for independent data it works under weaker conditions including allowing the heterogeneity in the bulk of the population eigenvalues than the existing approaches such as AIC and BIC. Simulation studies are also conducted to illustrate its good performance.
【报告时间】：2025年07月02日14:00-15:00
【报告地点】：位育楼417
