  Neural networks are suggested for learning a map from $d$-dimensional samples
with any underlying dependence structure to multivariate uniformity in $d'$
dimensions. This map, termed DecoupleNet, is used for dependence model
assessment and selection. If the data-generating dependence model was known,
and if it was among the few analytically tractable ones, one such
transformation for $d'=d$ is Rosenblatt's transform. DecoupleNets have multiple
advantages. For example, they only require an available sample and are
applicable to $d'<d$, in particular $d'=2$. This allows for simpler model
assessment and selection, both numerically and, because $d'=2$, especially
graphically. A graphical assessment method has the advantage of being able to
identify why, or in which region of the domain, a candidate model does not
provide an adequate fit, thus leading to model selection in particular regions
of interest or improved model building strategies in such regions. Through
simulation studies with data from various copulas, the feasibility and validity
of this novel DecoupleNet approach is demonstrated. Applications to real world
data illustrate its usefulness for model assessment and selection.
