  A new robust class of multivariate skew distributions is introduced.
Practical aspects such as parameter estimation method of the proposed class are
discussed, we show that the proposed class can be fitted under a reasonable
time frame. Our study shows that the class of distributions is capable to model
multivariate skewness structure and does not suffer from the curse of
dimensionality as heavily as other distributions of similar complexity do, such
as the class of canonical skew distributions. We also derive a nested form of
the proposed class which appears to be the most flexible class of multivariate
skew distributions in literature that has a closed-form density function.
Numerical examples on two data sets, i) a data set containing daily river flow
data recorded in the UK; and ii) a data set containing biomedical variables of
athletes collected by the Australian Institute of Sports (AIS), are
demonstrated. These examples further support the practicality of the proposed
class on moderate dimensional data sets.
