标题： 关于A. Azzalini、R. Browne、M. Genton和P. McNicholas的论文《论术语及偏斜分布两种表述的相对优劣》的评论 显示英文标题

标题： Comment on "On Nomenclature, and the Relative Merits of Two Formulations of Skew Distributions" by A. Azzalini, R. Browne, M. Genton, and P. McNicholas

摘要： 我们对Azzalini等人（2015年）最近发表的文章进行了评论，该文章讨论了文献中提出的两种不同分布，用于建模具有非对称性和可能长尾簇的数据。它们被Lee和McLachlan（2013a）称为受限和非受限偏斜正态分布及偏斜t-分布。我们澄清了Azzalini等人（2015年）对这种命名法的一个明显的误解，以区分这两种模型。此外，我们注意到McLachlan和Lee（2014年）对于两个数据集的分析中，非受限模型获得了比Azzalini等人（2015年）报告的结果更好的结果，这两个数据集构成了他们关于受限与非受限模型相对优越性的主张的基础。关于这两种模型的相对优越性，Lee和McLachlan（2014b, 2016）展示了如何使用属于更广泛类别的规范基本偏斜t（CFUST）类的分布来拟合数据，而不需要比非受限分布更多的计算努力。CFUST类包含了受限和非受限分布作为特例。因此，用户现在可以选择让数据决定哪个模型更适合他们的特定数据集。 显示英文摘要

摘要： We comment on the recent paper by Azzalini et al. (2015) on two different distributions proposed in the literature for the modelling of data that have asymmetric and possibly long-tailed clusters. They are referred to as the restricted and unrestricted skew normal and skew t-distributions by Lee and McLachlan (2013a). We clarify an apparent misunderstanding in Azzalini et al.(2015) of this nomenclature to distinguish between these two models. Also, we note that McLachlan and Lee (2014) have obtained improved results for the unrestricted model over those reported in Azzalini et al. (2015) for the two datasets that were analysed by them to form the basis of their claimson the relative superiority of the restricted and unrestricted models. On this matter of the relative superiority of these two models, Lee and McLachlan (2014b, 2016) have shown how a distribution belonging to the broader class, the canonical fundamental skew t (CFUST) class, can be fitted with little additional computational effort than for the unrestricted distribution. The CFUST class includes the restricted and unrestricted distributions as special cases. Thus the user now has the option of letting the data decide as to which model is appropriate for their particular dataset.