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Dependence model assessment and selection with DecoupleNets

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  • Marius Hofert
  • Avinash Prasad
  • Mu Zhu

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'

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  • Marius Hofert & Avinash Prasad & Mu Zhu, 2022. "Dependence model assessment and selection with DecoupleNets," Papers 2202.03406, arXiv.org, revised Oct 2022.
    • Handle: RePEc:arx:papers:2202.03406
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