VDC amortizes vine copula construction by reusing a single trained denoising model across edges plus IPFP projection, yielding competitive density and mutual information estimates with faster high-dimensional fitting.
McNeil, and Daniel Straumann
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
Bayesian EVT with Hawkes-AR-Gumbel dependence estimates CVaR up to 99.995% on simulated operational risk data and outperforms independent and shared-factor baselines.
citing papers explorer
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Amortized Vine Copulas for High-Dimensional Density and Information Estimation
VDC amortizes vine copula construction by reusing a single trained denoising model across edges plus IPFP projection, yielding competitive density and mutual information estimates with faster high-dimensional fitting.
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Bayesian Extreme Value Theory with Hawkes-AR-Gumbel Dependence for Extreme CVaR Estimation in Operational Risk
Bayesian EVT with Hawkes-AR-Gumbel dependence estimates CVaR up to 99.995% on simulated operational risk data and outperforms independent and shared-factor baselines.