Title：CP-factorization for high dimensional tensor time series and double projection iterations
Speaker： Jinyuan Chang （Southwestern University of Finance and Economics）
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 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.
Time：1.22（Thursday），10:00-11:00
Venue：Gewu Building 315
About the Speaker：常晋源，西南财经大学光华首席教授、中国科学院数学与系统科学研究院研究员，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、计量经济学和运筹管理国际顶级学术期刊Journal of the Royal Statistical Society Series B、Journal of Business & Economic Statistics、Journal of the American Statistical Association和Operations Research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教师奖一等奖和青年科学奖一等奖、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。
