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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 |
| 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 |
| Reference manual: | RTFA.pdf |
| Package source: | RTFA_0.1.0.tar.gz |
| Windows binaries: | r-devel: RTFA_0.1.0.zip, r-release: RTFA_0.1.0.zip, r-oldrel: RTFA_0.1.0.zip |
| macOS binaries: | r-release (arm64): RTFA_0.1.0.tgz, r-oldrel (arm64): RTFA_0.1.0.tgz, r-release (x86_64): RTFA_0.1.0.tgz, r-oldrel (x86_64): RTFA_0.1.0.tgz |
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