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

Citation