  Skew-symmetric densities recently received much attention in the literature,
giving rise to increasingly general families of univariate and multivariate
skewed densities. Most of those families, however, suffer from the inferential
drawback of a potentially singular Fisher information in the vicinity of
symmetry. All existing results indicate that Gaussian densities (possibly after
restriction to some linear subspace) play a special and somewhat intriguing
role in that context. We dispel that widespread opinion by providing a full
characterization, in a general multivariate context, of the information
singularity phenomenon, highlighting its relation to a possible link between
symmetric kernels and skewing functions -- a link that can be interpreted as
the mismatch of two densities.
