Mixtures of restricted skew-t factor analyzers with common factor loadings

Wan-Lun Wang, Luis M. Castro, Yen-Ting Chang and Tsung-I Lin ()
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Wan-Lun Wang: Feng Chia University
Luis M. Castro: Pontificia Universidad Católica de Chile
Yen-Ting Chang: National Chung Hsing University
Tsung-I Lin: National Chung Hsing University

Advances in Data Analysis and Classification, 2019, vol. 13, issue 2, No 6, 445-480

Abstract: Abstract Mixtures of common t factor analyzers (MCtFA) have been shown its effectiveness in robustifying mixtures of common factor analyzers (MCFA) when handling model-based clustering of the high-dimensional data with heavy tails. However, the MCtFA model may still suffer from a lack of robustness against observations whose distributions are highly asymmetric. This paper presents a further robust extension of the MCFA and MCtFA models, called the mixture of common restricted skew-t factor analyzers (MCrstFA), by assuming a restricted multivariate skew-t distribution for the common factors. The MCrstFA model can be used to accommodate severely non-normal (skewed and leptokurtic) random phenomena while preserving its parsimony in factor-analytic representation and performing graphical visualization in low-dimensional plots. A computationally feasible expectation conditional maximization either algorithm is developed to carry out maximum likelihood estimation. The numbers of factors and mixture components are simultaneously determined based on common likelihood penalized criteria. The usefulness of our proposed model is illustrated with simulated and real datasets, and experimental results signify its superiority over some existing competitors.

Keywords: Clustering; Common factor loadings; Data reduction; ECME algorithm; Factor analyzer; Outliers; 62H25; 62H30 (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11634-018-0317-2

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