Robust fitting of mixtures of factor analyzers using the trimmed likelihood estimator
Li Yang, Sijia Xiang, Weixin Yao · Communications in Statistics - Simulation and Computation · 2015
Mixtures of factor analyzers (MFAs) have been popularly used to cluster the high-dimensional data. However, the traditional estimation method is based on the normality assumptions of random terms and thus is sensitive to outliers. In this article, we introduce a robust estimation procedure of MFAs using the trimmed likelihood estimator. We use a simulation study and a real data application to demonstrate the robustness of the trimmed estimation procedure and compare it with the traditional normality-based maximum likelihood estimate.