时间：10月26日12:30
地点：明德主楼729会议室
主讲人：郭绍俊
讲座题目：factor modeling for high dimensional functional time series
主讲人简介：
郭绍俊，中国人民大学统计与大数据学院长聘副教授。主要研究方向为高维数据分析。
研讨会摘要：
many economic and scientific problems involve the analysis of high-dimensional functional time series, where the number of functional variables (p) diverges as the number of serially dependent observations (n) increases. in this talk we present a novel functional factor model for high-dimensional functional time series that maintains and makes use of the functional and dynamic structure to achieve great dimension reduction and find the latent factor structure. to estimate the number of functional factors and the factor loadings, we propose a fully functional estimation procedure based on an eigenanalysis for a nonnegative definite matrix. our proposal involves a weight matrix to tackle the issue of heterogeneity, the rationality of which is illustrated by formulating the estimation from a novel regression perspective. asymptotic properties of the proposed method are studied under a high-dimensional regime. to enhance interpretability for near-zero factor loadings and to ensure the consistency when p is very large, we impose the sparsity assumption on the factor loading matrix and then develop a regularized estimation procedure with theoretical guarantees. finally, we demonstrate that our proposed estimators significantly outperform the competing methods through both simulations and applications to a u.k. temperature dataset and a japanese mortality dataset.