Extending mixtures of factor models using the restricted multivariate skew-normal distribution

JOURNAL OF MULTIVARIATE ANALYSIS

卷:143

Article

Lin, Tsung-I^1,2  McLachlan, Geoffrey J.³  Lee, Sharon X.³ 

[1] Natl Chung Hsing Univ, Inst Stat, Taichung 402, Taiwan

[2] China Med Univ, Dept Publ Hlth, Taichung 404, Taiwan

[3] Univ Queensland, Dept Math, St Lucia, Qld 4072, Australia

关键词: Clustering;    Data reduction;    ECM algorithm;    Factor analyzer;    rMSN distribution;    Skewness;   

DOI  :  10.1016/j.jmva.2015.09.025

来源: Elsevier

【 摘 要 】

The mixture of factor analyzers (MFA) model provides a powerful tool for analyzing high-dimensional data as it can reduce the number of free parameters through its factor-analytic representation of the component covariance matrices. This paper extends the MFA model to incorporate a restricted version of the multivariate skew-normal distribution for the latent component factors, called mixtures of skew-normal factor analyzers (MSNFA). The proposed MSNFA model allows us to relax the need of the normality assumption for the latent factors in order to accommodate skewness in the observed data. The MSNFA model thus provides an approach to model-based density estimation and clustering of high-dimensional data exhibiting asymmetric characteristics. A computationally feasible Expectation Conditional Maximization (ECM) algorithm is developed for computing the maximum likelihood estimates of model parameters. The potential of the proposed methodology is exemplified using both real and simulated data. (C) 2015 Elsevier Inc. All rights reserved.

【 授权许可】

Free