报告题目：Factor Modelling for Clustering High-dimensional Time Series
报 告 人：潘光明 教授
所在单位：新加坡南洋理工大学
报告时间：2022年07月15日 星期三上午 09：30-11：00
报告地点：腾讯会议：110-432-227, 会议密码：0715
点击链接入会，或添加至会议列表：https://meeting.tencent.com/dm/kfI7PMQY5Nff
校内联系人：张勇 zyong2661@jlu.edu.cn
报告摘要：We propose a new unsupervised learning method for clustering a large number of time series based on a latent factor structure. Each cluster is characterized by its own cluster-specific factors in addition to some common factors which impact on all the time series concerned. Our setting also offers the flexibility that some time series may not belong to any clusters. The consistency with explicit convergence rates is established for the estimation of the common factors, the cluster-specific factors, and the latent clusters. Numerical illustration with both simulated data as well as a real data example is also reported. As a spin-off, the proposed new approach also advances significantly the statistical inference for the factor model of Lam and Yao (2012). This is a joint work with B. Zhang, Q. W. Yao and W. Zhou.
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。
