Transformation Mixture Modeling for Skewed Data Groups with Heavy Tails and Scatter

• 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
• 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

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.