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- W3198463313 abstract "Copula-based models provide a great deal of flexibility in modelling multivariate distributions, allowing for the specifications of models for the marginal distributions separately from the dependence structure (copula) that links them to form a joint distribution. Choosing a class of copula models is not a trivial task and its misspecification can lead to wrong conclusions. We introduce a novel class of grid-uniform copula functions, which is dense in the space of all continuous copula functions in a Hellinger sense. We propose a Bayesian model based on this class which posterior distribution is strongly consistent and develop an automatic Markov chain Monte Carlo algorithm for exploring the corresponding posterior distribution. The methodology is illustrated by means of simulated data and compared to the main existing approach." @default.
- W3198463313 created "2021-09-13" @default.
- W3198463313 creator A5002233749 @default.
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- W3198463313 date "2023-01-01" @default.
- W3198463313 modified "2023-09-26" @default.
- W3198463313 title "Grid-Uniform Copulas and Rectangle Exchanges: Bayesian Model and Inference for a Rich Class of Copula Functions" @default.
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- W3198463313 doi "https://doi.org/10.1214/23-ba1396" @default.
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