【专家简介】 潘光明，新加坡南洋理工大学教授，博士生导师。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。至今已在Annals of Statistics、Journal of American Statistical Association、Journal of Royal Statistical Society（B）、 Annals of Probability、Annals of Applied Probability、Bernoulli、IEEE Transactions on Signal Processing、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 of International Statistical Institute)，《Random Matrices: Theory and Applications》杂志编委。
【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, 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 Zhang Bo, Yao Qiwei and Zhou Wang.
腾讯会议号: 237-527-695
报告时间：2022年10月20日(星期四)上午10:00