.png){kind=icon}
返回
TENSOR FACTOR MODEL ESTIMATION BY ITERATIVE PROJECTION
.png){kind=icon}
PRE
AI
DOI：10.1214/24-AOS2412.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
High-dimensional tensor data
factor model
orthogonal projection
time series
Tucker decomposition
期刊
IF:
3.7
论文数:
2.8K
被引数:
2.9W
机构
引用论文
DC Offset Error Compensation Algorithm for PR Current Control of a Single-Phase Grid-Tied Inverter.png)
Energies
OAAI
EnergiesIF0
OAAI分享收藏TENSOR CLUSTERING WITH PLANTED STRUCTURES: STATISTICAL OPTIMALITY AND COMPUTATIONAL LIMITSANNALS OF STATISTICSOAAI
ANNALS OF STATISTICSIF3.7
OAAI分享收藏分享收藏MULTILAYER TENSOR FACTORIZATION WITH APPLICATIONS TO RECOMMENDER SYSTEMSANNALS OF STATISTICS
OAAI
ANNALS OF STATISTICSIF3.7
OAAI分享收藏
查看更多内容