Stochastic Inversion of Multivariate Uniform-Distribution-Preserving Transformations

A multivariate transformation of the unit cube with component transformations that are piecewise continuously differentiable and uniform distribution preserving (udp) is considered.

A stochastic inverse transformation is defined using randomization to overcome the non-injective nature of the udp transformations.

The inverse transformation preserves the uniform margins of a random vector distributed according to a copula and yields different copulas for different randomizations.

A copula density transformation result for the multivariate stochastic inverse is proved and illustrated in the bivariate case.