RTFA

Description

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) <arXiv:2206.09800>, and Barigozzi et al. (2023) <arXiv:2303.18163>.

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Maintainer: ‘Lingxiao Li <lilingxiao@mail.sdu.edu.cn>’

The Description field contains
  discussed in Barigozzi et al. (2022) <arXiv:2206.09800>, and Barigozzi
  et al. (2023) <arXiv:2303.18163>.
Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.

Maintainer: ‘Lingxiao Li <lilingxiao@mail.sdu.edu.cn>’

The Description field contains
  discussed in Barigozzi et al. (2022) <arXiv:2206.09800>, and Barigozzi
  et al. (2023) <arXiv:2303.18163>.
Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.

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Maintainer: ‘Lingxiao Li <lilingxiao@mail.sdu.edu.cn>’

The Description field contains
  discussed in Barigozzi et al. (2022) <arXiv:2206.09800>, and Barigozzi
  et al. (2023) <arXiv:2303.18163>.
Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.

Maintainer: ‘Lingxiao Li <lilingxiao@mail.sdu.edu.cn>’

The Description field contains
  discussed in Barigozzi et al. (2022) <arXiv:2206.09800>, and Barigozzi
  et al. (2023) <arXiv:2303.18163>.
Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.

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