IALS: Iterative Alternating Least Square Estimation for
Large-Dimensional Matrix Factor Model
The matrix factor model has drawn growing attention for its advantage in achieving two-directional dimension reduction simultaneously for matrix-structured observations. In contrast to the Principal Component Analysis (PCA)-based methods, we propose a simple Iterative Alternating Least Squares (IALS) algorithm for matrix factor model, see the details in He et al. (2023) <doi:10.48550/arXiv.2301.00360>.
| Version: |
0.1.3 |
| Depends: |
R (≥ 4.0) |
| Imports: |
RSpectra, pracma, HDMFA |
| Published: |
2024-02-16 |
| Author: |
Yong He [aut],
Ran Zhao [aut, cre],
Wen-Xin Zhou [aut] |
| Maintainer: |
Ran Zhao <Zhaoran at mail.sdu.edu.cn> |
| License: |
GPL-2 | GPL-3 |
| NeedsCompilation: |
no |
| CRAN checks: |
IALS results |
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