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

Documentation:

Reference manual: REFA.pdf

Downloads:

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): not available

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