REFA: Robust Exponential Factor Analysis
A robust alternative to the traditional principal component estimator is proposed within the framework of factor models, known as Robust Exponential Factor Analysis, specifically designed for the modeling of high-dimensional datasets with heavy-tailed distributions. The algorithm estimates the latent factors and the loading by minimizing the exponential squared loss function. To determine the appropriate number of factors, we propose a modified rank minimization technique, which has been shown to significantly enhance finite-sample performance.
| Version: |
0.1.0 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
mvtnorm |
| Published: |
2023-11-19 |
| Author: |
Jiaqi Hu [cre, aut],
Xueqin Wang [aut] |
| Maintainer: |
Jiaqi Hu <hujiaqi at mail.ustc.edu.cn> |
| License: |
GPL-3 |
| NeedsCompilation: |
no |
| Materials: |
NEWS |
| CRAN checks: |
REFA results |
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