Dependence model assessment and selection with DecoupleNets

Marius Hofert, Avinash Prasad and Mu Zhu

Papers from arXiv.org

Abstract: 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'

Date: 2022-02, Revised 2022-10
New Economics Papers: this item is included in nep-big and nep-cmp
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