时 间：2025年11月27 日（周四）14：00 – 15：00
地 点：普陀校区理科大楼A1514室
报告人：虞龙 上海财经大学副教授
主持人：张心雨 华东师范大学副教授
摘 要：
We adopt the canonical polyadic (CP) decomposition to model high-dimensional tensor time series. Our primary goal is to identify and estimate the factor loadings in the CP decomposition. We propose a one-pass estimation procedure through standard eigen-analysis for a matrix constructed based on the serial dependence structure of the data. The asymptotic properties of the proposed estimator are established under a general setting as long as the factor loading vectors are algebraically linear independent, allowing the factors to be correlated and the factor loading vectors to be not nearly orthogonal. The procedure adapts to the sparsity of the factor loading vectors, accommodates weak factors, and demonstrates strong performance across a wide range of scenarios. A tractable limiting representation of the estimator is derived, which plays a key role in the related inference problems. To further reduce estimation errors, we also introduce an iterative algorithm based on a novel double projection approach. We theoretically justify the improved convergence rate of the iterative estimator, and also provide the associated limiting distribution. All results are validated through extensive simulations and a real data application.
报告人简介：
虞龙，现任上海财经大学统计与数据科学学院副教授。2020年6月毕业于复旦大学管理学院，获理学博士学位；曾赴美国密歇根大学安娜堡分校联合培养，在新加坡国立大学统计与数据科学系从事博士后研究工作；2022入职上海财经大学工作至今。他的主要研究方向是多元统计分析，包括因子模型、随机矩阵、稳健统计、高维数据等，相关研究成果发表AOS, JASA, Biometrika, JoE，JBES等，主持国家自然科学基金青年基金1项，教育部引才项目1项，上海市浦江人才计划A类项目1项。