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Activity Number: 272 - Approaches in Clustering for Analysis of Emerging Data Types
Type: Topic Contributed
Date/Time: Tuesday, August 9, 2022 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #322079
Title: Transformation Mixture Modeling for Skewed Data Groups with Heavy Tails and Scatter
Author(s): Xuwen Zhu* and Volodymyr Melnykov and Yana Melnykov
Companies: The University of Alabama and The University of Alabama and The University of Alabama
Keywords: finite mixture model; cluster analysis; transformation to normality; symmetry

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.

Authors who are presenting talks have a * after their name.

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