Bayesian model selection of vine copulas: a loss-based perspective

arXiv:2606.21512 · stat.ME, stat.ML · Submitted 2026-06-19 · Read on arXiv

Rosario Barone, Luciana Dalla Valle, Fabrizio Leisen, Cristiano Villa

stat.ME, stat.ML

Submitted: 2026-06-19

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Copula — 165× in this paper · explained in 2 episodes
• Sparsity — 15× in this paper · explained in 5 episodes
• Shotgun Stochastic Search — 7× in this paper
• Bayesian Framework — 4× in this paper · explained in 3 episodes
• Kullback–Leibler Divergence — 4× in this paper · explained in 2 episodes
• Consistency — 3× in this paper · explained in 3 episodes
• Lasso — 3× in this paper · explained in 2 episodes
• Loss Function — 3× in this paper · explained in 2 episodes
• Bayesian Inference — 2× in this paper · explained in 6 episodes
• Bayesian Information Criterion — 2× in this paper
• Accuracy — 1× in this paper · explained in 1 episode
• Bayesian Additive Regression Trees — 1× in this paper
• Calibration — 1× in this paper · explained in 9 episodes
• Editor — 1× in this paper · explained in 2 episodes
• Papers — 1× in this paper · explained in 3 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Sufficiency — 1× in this paper · explained in 2 episodes

Sources

• Model selection tests for truncated vine copulas under nested hypotheses
• Trunc-Opt vine building algorithms
• Loss-based prior for CART and BART models
• Throwing Vines at the Wall: Structure Learning via Random Search

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