报告时间：2021年10月12日 19：00-20：00

腾讯会议：217 483 666

报告人：夏强（华南农业大学）

报告摘要：To deal with the factor analysis for high-dimensional stationary time series, this paper suggests a novel method that integrates three ideas. First, based on the eigenvalues of a non-negative definite matrix, we propose a new approach for consistently determining the number of factors. The proposed method is computationally efficient with a single step procedure, especially when both weak and strong factors exist in the factor model. Second, a fresh measurement of the difference between the factor loading matrix and its estimate is recommended to overcome the nonidentifiability of the loading matrix due to any geometric rotation. The asymptotic results of our proposed method are also studied under this measurement, which enjoys “blessing of dimensionality. Finally, with the estimated factors, the latent vector autoregressive (VAR) model is analyzed such that the convergence rate of the estimated coefficients is as fast as when the samples of VAR model are observed. In support of our results on consistency and computational efficiency, the finite sample performance of the proposed method is examined by simulations and the analysis of one real data example.

报告人介绍：

夏强，教授，博士生导师。2015年获中国人民大学统计学院博士学位，2006年至今在华南农业大学工作，现为数学与信息学院、软件学院副院长。近年来主持国家自然科学基金重大研究计划培育项目，国家自然科学基金面上项目，国家社科基金青年项目，教育部人文社科基金规划项目，广东省自然科学基金面上项目，全国统计科学研究重点项目各1项。在国内外学术期刊上发表论文30余篇，发表专著2部。担任美国数学评论评论员；担任中国商业统计会、中国现场统计研究会资源与环境，生存分析，大数据分析分会理事；担任广东省统计学会、广东省现场统计学会常务理事。

邀请人:马学俊