  This paper derives the elliptical matrix variate version of the well known
univariate Birnbaum and Saunders distribution. A generalisation based on a
matrix transformation is proposed, instead of the independent element by
element representation of the Gaussian univariate version of 1969. New results
on Jacobians were needed to derived the matrix variate distribution. A number
of special cases are studied and some basic properties are found. Finally, an
example based on real data of two populations is provided. The maximum
likelihood estimates are found for a number of matrix variate generalised
Birnbaum-Saunders distributions based on Kotz models. A comparison with the
Gaussian kernel is also given by using a modified BIC criterion.
