An overview of skew distributions in model-based clustering. (English) Zbl 07451362

Summary: 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.


62H30 Classification and discrimination; cluster analysis (statistical aspects)
62F86 Parametric inference and fuzziness
Full Text: DOI


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