报告人：刘一鸣 副教授
报告题目：Identify the source of spikes: factor or mixture?
报告时间：2025年11月18日（周二）11:00-12:00
报告地点：云龙校区6号楼304会议室
主办单位：数学与统计学院、数学研究院、科学技术研究院
报告人简介：
刘一鸣，暨南大学经济学院副教授。目前主要研究方向：机器学习、随机矩阵、经验似然及其相关应用等。主持国自然科学基金，广东省自然科学面上基金等项目。至今已在IEEE Transactions on Information Theory, Bernoulli, Statistica Sinica, Scandinavian Journal of Statistics等杂志发表论文15篇。
报告摘要：
We consider the problem of identifying the pattern of latent variables in high-dimensional linear latent variable models, which can also be interpreted as determining the source of spiked singular values in the data matrix. Specifically, we test whether the latent variables are continuous or categorical, a distinction which is crucial for data interpretation but challenging when the dimensionality is comparable to the sample size. To address this inference problem, we analyze the asymptotic behavior of empirical measures associated with singular vectors corresponding to large spiked singular values. Leveraging these insights, we propose a novel test statistic based on the eigenvector quantile differences and establish its theoretical performance under the null hypothesis. Simulation studies and real data analyses for breast cancer and glioblastoma gene expression datasets demonstrate the effectiveness and practical utility of our method.
