A0775
Title: Transformation mixture modeling for skewed data groups with heavy tails and scatter
Authors: Yana Melnykov - The University of Alabama (United States) [presenting]
Volodymyr Melnykov - The University of Alabama (United States)
Xuwen Zhu - University of Alabama (United States)
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 modelling skewness or heavy tails have been considered in this context recently. A novel contaminated transformation mixture model is proposed that is constructed based on the idea of transformation to symmetry and can account for skewness and heavy tails and automatically assign scatter to secondary components.
