Informative goodness-of-fit for multivariate distributions

This article introduces an informative goodness-of-fit (iGOF) approach to study multivariate distributions. When the null model is rejected, iGOF allows us identify underlying sources of mismodeling and naturally equips practitioners with additional insights on nature deviations from true distribution. The character procedure achieved by exploiting smooth tests random fields theory facilitate analysis data. Simulation studies show that enjoys high power for different types alternatives. methods presented here directly address problem background arising in physics astronomy. It these areas motivation this work rooted.