With the progress of information technology, large amounts of asymmetric, leptokurtic and heavy-tailed data are arising in various fields, such as finance, engineering, genetics and medicine. It is very challenging to model those kinds of data, especially for extremely skewed data, accompanied by very high kurtosis or heavy tails. In this paper, we propose a class of novel skewed generalized t distribution (SkeGTD) as a scale mixture of skewed generalized normal. The proposed SkeGTD has excellent adaptiveness to various data, because of its capability of allowing for a large range of skewness and kurtosis and its compatibility of the separated location, scale, skewness and shape parameters. We investigate some important properties of this family of distributions. The maximum likelihood estimation, L-moments estimation and two-step estimation for the SkeGTD are explored. To illustrate the usefulness of the proposed methodology, we present simulation studies and analyze two real datasets.

翻译：随着信息技术的进步，金融、工程、遗传学及医学等领域涌现出大量非对称、尖峰厚尾数据。对此类数据的建模极具挑战性，尤其是对伴随极高峰度或厚尾特征的极端偏斜数据。本文提出一类新型偏斜广义t分布（SkeGTD），将其构建为偏斜广义正态分布的尺度混合。该分布因允许大范围的偏度与峰度参数，并兼容分离的位置、尺度、偏度及形状参数，对各类数据展现出卓越的适应性。我们研究了该分布族的重要性质，并探讨了SkeGTD的极大似然估计、L矩估计及两步估计方法。通过模拟研究及两个真实数据集分析，验证了所提方法的实用性。