Dependence Model Assessment and Selection with DecoupleNets
Marius Hofert, Avinash Prasad, Mu Zhu · Journal of Computational and Graphical Statistics · 2022
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′