Journal
JOURNAL OF MULTIVARIATE ANALYSIS
Volume 188, Issue -, Pages -Publisher
ELSEVIER INC
DOI: 10.1016/j.jmva.2021.104853
Keywords
Flexible distributions; Mixture models; Skew distributions; Transformation
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Funding
- Australian Research Council [IC170100035, DP180101192]
- Australian Research Council [IC170100035] Funding Source: Australian Research Council
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The literature on non-normal model-based clustering has been expanding in recent years. These models often use a mixture of component densities to provide flexibility in distributional shapes and handle skewness. Skewing is typically achieved by introducing latent variables or considering marginal transformations of the original variables.
The literature on non-normal model-based clustering has continued to grow in recent years. The non-normal models often take the form of a mixture of component densities that offer a high degree of flexibility in distributional shapes. They handle skewness in different ways, most typically by introducing latent 'skewing' variable(s), while some other consider marginal transformations of the original variable(s). We provide a selective overview of the main types of skew distributions used in the area, based on their characterization of skewness, and discuss different skew shapes they can produce. For brevity, we focus on the more commonly-used families of distributions. Crown Copyright (C) 2021 Published by Elsevier Inc. All rights reserved.
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