The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
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 |
| 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 |
Please use the canonical form https://CRAN.R-project.org/package=REFA to link to this page.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.