Contamination transformation matrix mixture modeling for skewed data groups with heavy tails and scatter

Xuwen Zhu, Yana Melnykov () and Angelina S. Kolomoytseva
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Xuwen Zhu: The University of Alabama
Yana Melnykov: The University of Alabama
Angelina S. Kolomoytseva: The University of Alabama

Advances in Data Analysis and Classification, 2024, vol. 18, issue 1, No 5, 85-101

Abstract: Abstract Model-based clustering is a popular application of the rapidly developing area of finite mixture modeling. While there is ample work focusing on clustering multivariate data, an increasing number of advancements have been aiming at the expansion of existing theory to the matrix-variate framework. Matrix-variate Gaussian mixtures are most popular in this setting despite the potential misfit for skewed and heavy-tailed data. To overcome this lack of flexibility, a new contaminated transformation matrix mixture model is proposed. We illustrate its utility in a series of experiments on simulated data and apply to a real-life data set containing COVID-related information. The performance of the developed model is promising in all considered settings.

Keywords: Cluster analysis; Finite mixture model; Matrix-variate distribution; Skewness; Transformation; 62H30 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11634-023-00550-w

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