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    报告题目:Two-way Dynamic Factor Models for High-Dimensional Matrix-valued Time Series

    报 告 人:郭建华教授

    报告时间: 2021年10月30日(周六)10:00-11:30





    郭建华,东北师范大学副校长,教授,博士生导师。国务院学位委员会学科评议组统计学科召集人,教育部科技委委员,国家杰出青年科学基金获得者,教育部“长江学者奖励计划”特聘教授,“新世纪百千万人才工程”国家级人选,国务院政府特殊津贴获得者,IMS Fellow,ISI Elected Member,国家社会科学基金学科规划评议组成员,国家自然科学基金会评专家,著名期刊JASA、《统计研究》等的编委。


    In this talk, I will introduce a two-way dynamic factor model (2w-DFM) for high-dimensional matrix-valued time series and study some of the basic theoretical properties in terms of identifiability and estimation accuracy. The proposed model aims to capture separable and low-dimensional effects of row and column attributes and their correlations across rows, columns, and time points. Complementary to other dynamic factor models for high dimensional data, the 2w-DFM inherits the dimension-reduction feature of factor models but assumes additive row and column factors for easier interpretability. We provide conditions to ensure model identifiability, and consider a pseudo-likelihood-based two-step method for parameter estimation. Under an asymptotic regime where the size of the data matrices as well as the length of the time series increase, we establish that the estimators achieve the optimal rate of convergence and are asymptotically normal. The asymptotic properties are reaffirmed empirically through simulation studies. An application to air quality data in Chinese cities is given to illustrate the merit of the 2w-DFM.




    撰稿:孙晓霞 审核:富宇 单位:数据科学与人工智能学院

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