Transformation mixture modeling for skewed data groups with heavy tails and scatter

Yana Melnykov, Xuwen Zhu () and Volodymyr Melnykov
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Yana Melnykov: The University of Alabama
Xuwen Zhu: The University of Alabama
Volodymyr Melnykov: The University of Alabama

Computational Statistics, 2021, vol. 36, issue 1, No 3, 78 pages

Abstract: Abstract For decades, Gaussian mixture models have been the most popular mixtures in literature. However, the adequacy of the fit provided by Gaussian components is often in question. Various distributions capable of modeling skewness or heavy tails have been considered in this context recently. In this paper, we propose a novel contaminated transformation mixture model that is constructed based on the idea of transformation to symmetry and can account for skewness, heavy tails, and automatically assign scatter to secondary components.

Keywords: Finite mixture model; Cluster analysis; Transformation to normality; Symmetry (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s00180-020-01009-8

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