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stat.ME2026
Bayesian model selection of vine copulas: a loss-based perspective
Rosario Barone, Luciana Dalla Valle, Fabrizio Leisen +1
The growing popularity of vine copulas in multivariate statistical analysis is largely driven by their ability to capture complex dependence structures. However, this flexibility c…
stat.ME2026
The General Formulation of Loss-Based Priors for Parameter Spaces
Cristiano Villa
Loss-based priors assign probability mass to parameter values according to the inferential loss incurred when they are excluded from the parameter space, and provide a general solu…