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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 |
| DOI: | 10.32614/CRAN.package.REFA |
| 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 |
| Reference manual: | REFA.pdf |
| Package source: | REFA_0.1.0.tar.gz |
| Windows binaries: | r-devel: REFA_0.1.0.zip, r-release: REFA_0.1.0.zip, r-oldrel: REFA_0.1.0.zip |
| macOS binaries: | r-release (arm64): REFA_0.1.0.tgz, r-oldrel (arm64): REFA_0.1.0.tgz, r-release (x86_64): REFA_0.1.0.tgz, r-oldrel (x86_64): REFA_0.1.0.tgz |
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