報告人：虞龍 教授

報告題目：CP-Factorization for High Dimensional Tensor Time Series and Double Projection Iterations

報告時間：2026年4月13日（周一）16:20-17:00

報告地點：云龍校區(qū)6號樓304報告廳

主辦單位：數(shù)學(xué)與統(tǒng)計學(xué)院、數(shù)學(xué)研究院、科學(xué)技術(shù)研究院

報告人簡介：

虞龍，現(xiàn)任上海財經(jīng)大學(xué)統(tǒng)計與數(shù)據(jù)科學(xué)學(xué)院副教授。2020年6月畢業(yè)于復(fù)旦大學(xué)管理學(xué)院，獲理學(xué)博士學(xué)位；曾赴美國密歇根大學(xué)安娜堡分校聯(lián)合培養(yǎng)，在新加坡國立大學(xué)統(tǒng)計與數(shù)據(jù)科學(xué)系從事博士后研究工作；2022入職上海財經(jīng)大學(xué)工作至今。他的主要研究方向是多元統(tǒng)計分析，包括因子模型、隨機矩陣、穩(wěn)健統(tǒng)計、高維數(shù)據(jù)等，相關(guān)研究成果發(fā)表AOS, JASA, Biometrika, JoE，JBES等，主持國家自然科學(xué)基金青年基金1項，教育部引才項目1項，上海市浦江人才計劃A類項目1項。

報告摘要：

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.