報告題目：Factor Modelling for Clustering High-dimensional Time Series
報 告 人：潘光明 教授
所在單位：新加坡南洋理工大學
報告時間：2022年07月15日 星期三上午 09：30-11：00
報告地點：騰訊會議：110-432-227, 會議密碼：0715
點擊鍊接入會，或添加至會議列表：https://meeting.tencent.com/dm/kfI7PMQY5Nff
校内聯系人：張勇 zyong2661@jlu.edu.cn
報告摘要：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.
報告人簡介：潘光明，新加坡南洋理工大學教授。2005年7月博士畢業于中國科學技術大學，自2008年以來，在新加坡南洋理工大學工作。研究領域包括高維統計推斷、随機矩陣理論、多元統計、應用概率等，至今在統計學和概率論的頂級雜志，如: Annals of Statistics, Journal of American Statistical Association, Journal of Royal Statistical Society（B）, 《Annals of Probability》、《Annals of Applied Probability》、《Bernoulli》等上發表論文50餘篇。現為國際統計學會會員(Elected Member of International Statistical Institute)。擔任《Random Matrices: Theory and Applications》雜志編委。
