Interest in the skew-normal and related families of distributions has grown enormously over
recent years, as theory has advanced, challenges of data have grown, and computational …

Clustering is the process of finding underlying group structures in data. Although mixture
model-based clustering is firmly established in the multivariate case, there is a relative …

Although there is ample work in the literature dealing with skewness in the multivariate
setting, there is a relative paucity of work in the matrix variate paradigm. Such work is, for …

Three-way data can be conveniently modelled by using matrix variate distributions. Although
there has been a lot of work for the matrix variate normal distribution, there is little work in the …

In recent years, data have become increasingly higher dimensional and, therefore, an
increased need has arisen for dimension reduction techniques for clustering. Although such …

The skew normal distribution is an extension of the normal distribution allowing for the
presence of skewness. Such a distribution can model skewed data in many applied fields …

Over the years, data have become increasingly higher dimensional, which has prompted an
increased need for dimension reduction techniques. This is perhaps especially true for …

We establish an identity for E f YE f X, when X and Y both have matrix variate skew-normal
distributions and the function f satisfies some weak conditions. The characteristic function of …

Typical multivariate analysis assumes independence among the individual observations as
well as elliptical symmetry of distributions. In many situations these assumptions may be too …

It was Azzalini (1985) who introduced the univariate skew normal distribution family with a
shape parameter, and then extended skew normal distribution family by adding an …