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) <arXiv:2206.09800>, and Barigozzi et al. (2023) <arXiv:2303.18163>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.0 |
rolling linux/jammy R-4.5 | RTFA_0.1.0.tar.gz |
32.7 KiB |
0.1.0 |
rolling linux/noble R-4.5 | RTFA_0.1.0.tar.gz |
32.6 KiB |
0.1.0 |
rolling source/ R- | RTFA_0.1.0.tar.gz |
4.7 KiB |
0.1.0 |
latest linux/jammy R-4.5 | RTFA_0.1.0.tar.gz |
32.7 KiB |
0.1.0 |
latest linux/noble R-4.5 | RTFA_0.1.0.tar.gz |
32.6 KiB |
0.1.0 |
latest source/ R- | RTFA_0.1.0.tar.gz |
4.7 KiB |
0.1.0 |
2026-04-26 source/ R- | RTFA_0.1.0.tar.gz |
4.7 KiB |
0.1.0 |
2026-04-23 source/ R- | RTFA_0.1.0.tar.gz |
4.7 KiB |
0.1.0 |
2026-04-09 windows/windows R-4.5 | RTFA_0.1.0.zip |
35.0 KiB |
0.1.0 |
2025-04-20 source/ R- | RTFA_0.1.0.tar.gz |
4.7 KiB |