RTFA: Robust Factor Analysis for Tensor Time Series
Tensor Factor Models (TFM) are appealing dimension reduction tools for high-order tensor time series, and have wide applications in economics, finance and medical imaging. We propose an one-step projection estimator by minimizing the least-square loss function, and further propose a robust estimator with an iterative weighted projection technique by utilizing the Huber loss function. The methods are discussed in Barigozzi et al. (2022) <doi:10.48550/arXiv.2206.09800>, and Barigozzi et al. (2023) <doi:10.48550/arXiv.2303.18163>.
| Version: |
0.1.0 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
rTensor, tensor |
| Published: |
2023-04-10 |
| DOI: |
10.32614/CRAN.package.RTFA |
| Author: |
Matteo Barigozzi [aut],
Yong He [aut],
Lorenzo Trapani [aut],
Lingxiao Li [aut, cre] |
| Maintainer: |
Lingxiao Li <lilingxiao at mail.sdu.edu.cn> |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: |
no |
| In views: |
TimeSeries |
| CRAN checks: |
RTFA results |
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