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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. For more detail of Robust Exponential Factor Analysis, please refer to Hu et al. (2026) <doi:10.1016/j.jmva.2025.105567>.

Versions across snapshots

VersionRepositoryFileSize
0.2.0 rolling linux/jammy R-4.5 REFA_0.2.0.tar.gz 38.4 KiB
0.2.0 rolling linux/noble R-4.5 REFA_0.2.0.tar.gz 38.3 KiB
0.2.0 rolling source/ R- REFA_0.2.0.tar.gz 5.8 KiB
0.2.0 latest linux/jammy R-4.5 REFA_0.2.0.tar.gz 38.4 KiB
0.2.0 latest linux/noble R-4.5 REFA_0.2.0.tar.gz 38.3 KiB
0.2.0 latest source/ R- REFA_0.2.0.tar.gz 5.8 KiB
0.2.0 2026-04-26 source/ R- REFA_0.2.0.tar.gz 5.8 KiB
0.2.0 2026-04-23 source/ R- REFA_0.2.0.tar.gz 5.8 KiB
0.2.0 2026-04-09 windows/windows R-4.5 REFA_0.2.0.zip 40.9 KiB
0.1.0 2025-04-20 source/ R- REFA_0.1.0.tar.gz 5.0 KiB

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