TENSOR FACTOR MODEL ESTIMATION BY ITERATIVE PROJECTION

• 成果类型: Article
• 署名作者: Han, Yuefeng; Chen, Rong; Yang, Dan; Zhang, Cun-Hui
• 署名单位: University of Notre Dame; Rutgers University System; Rutgers University New Brunswick; University of Hong Kong
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/24-AOS2412
• 发表日期: 2024
• 页码: 2641-2667
• 关键词: Principal component analysis; large covariance estimation; large hidden clique; time-series; number; matrix

摘要

Tensor time series, which is a time series consisting of tensorial observations, has become ubiquitous. It typically exhibits high dimensionality. One approach for dimension reduction is to use a factor model structure, in a form similar to Tucker tensor decomposition, except that the time dimension is treated as a dynamic process with a time dependent structure. In this paper, we introduce two approaches to estimate such a tensor factor model by using iterative orthogonal projections of the original tensor time series. These approaches extend the existing estimation procedures and improve the estimation accuracy and convergence rate significantly as proven in our theoretical investigation. Our algorithms are similar to the higher-order orthogonal projection method for tensor decomposition, but with significant differences due to the need to unfold tensors in the iterations and the use of autocorrelation. Consequently, our analysis is significantly different from the existing ones. Computational and statistical lower bounds are derived to prove the optimality of the sample size requirement and convergence rate for the proposed methods. Simulation study is conducted to further illustrate the statistical properties of these estimators.