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
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

Documentation:

Reference manual: RTFA.pdf

Downloads:

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

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