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.

中文翻译：

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具有大量尾部和散点的偏斜数据组的转换混合建模

几十年来，高斯混合模型一直是文献中最受欢迎的混合模型。但是，高斯分量所提供的拟合是否足够通常是个问题。在此背景下，最近已经考虑了各种能够模拟偏斜度或重尾巴的分布。在本文中，我们提出了一种新的受污染的转化混合模型，该模型基于将对称转化为对称的思想而构建，并且可以解决偏斜，粗尾和自动将散射分配给次要成分的问题。