Comment on "Comparing two formulations of skew distributions with   special reference to model-based clustering" by A. Azzalini, R. Browne, M.   Genton, and P. McNicholas
  In this paper, we comment on the recent comparison in Azzalini et al. (2014) of 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 t-distributions by Lee and McLachlan (2013a). Firstly, we wish to point out that in Lee and McLachlan (2014b), which preceded this comparison, it is shown how a distribution belonging to the broader class, the canonical fundamental skew t (CFUST) class, can be fitted with essentially no 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. Secondly, we wish to identify several statements in the comparison by Azzalini et al.(2014) that demonstrate a serious misunderstanding of the reporting of results in Lee and McLachlan (2014a) on the relative performance of these two skew t-distributions. In particular, there is an apparent misunderstanding of the nomenclature that has been adopted to distinguish between these two models. Thirdly, we take the opportunity to report here that we have obtained improved fits, in some cases a marked improvement, for the unrestricted model for various cases corresponding to different combinations of the variables in the two real datasets that were used in Azzalini et al. (2014) to mount their claims on the relative superiority of the restricted and unrestricted models. For one case the misclassification rate of our fit under the unrestricted model is less than one third of their reported error rate. Our results thus reverse their claims on the ranking of the restricted and unrestricted models in such cases. 
Geoffrey J. McLachlan and Sharon X. Lee
stat.ME