Robust Factor Analysis for Tensor Time Series
Overview
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>.
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People & History
- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- RR 4.3.0 released · 2023-04-21
- 0.1.0Latest2023-04-10 · current release
- RR 4.2.0 released · 2022-04-22
Package metadata
- First published
- 2023-04-10
- Total releases
- 1 / 3 yrs
- License
- GPL (>= 2) OSI
- Minimum R
- ≥ 3.5.0
- Download size
- 4.7 KB
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