主讲人Speaker: 常晋源    教授     西南财经大学

时间Date & Time: 2026年5月15日（周五），14:30--15:30

地点Venue:粤海校区汇星楼565会议室

内容简介/ Abstract:

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 linearly 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. 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 derive the associated limiting distribution. A consistent estimator of the asymptotic variance is also provided, which plays a key role in the related inference problems. All results are validated through extensive simulations and two real data applications.

主讲人介绍/Biography of the speaker：

常晋源，西南财经大学光华特聘教授，国家杰出青年科学基金获得者、四川省特聘专家、四川省统计专家咨询委员会委员。主要从事大规模复杂数据分析相关的研究，先后担任统计学和计量经济学国际顶级学术期刊Journal of the Royal Statistical Society Series B、Journal of Business & Economic Statistics、Journal of the American Statistical Association的副主编，获得过国务院政府特殊津贴、霍英东教育基金会高等院校青年科学奖一等奖、教育部高等学校科学研究优秀成果奖、四川省青年科技奖等多项奖励。