Robust fitting of mixture of factor analyzers using the trimmed likelihood estimator
Date
2014-07-21
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Kansas State University
Abstract
Mixtures of factor analyzers 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 mixtures of factor analyzers using the trimmed likelihood estimator (TLE). 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.
Description
Keywords
EM algorithm, Factor analysis, Mixture models, Robustness, Trimmed likelihood estimator
Graduation Month
August
Degree
Master of Science
Department
Department of Statistics
Major Professor
Weixin Yao
Date
2014
Type
Report