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In mixed company: Bayesian inference for bivariate conditional copula models with discrete and continuous outcomes. (English) Zbl 1244.62031

Summary: Conditional copula models are flexible tools for modelling complex dependence structures in regression settings. We construct Bayesian inference for the conditional copula model adapted to regression settings in which the bivariate outcome is continuous or mixed. The dependence between the copula parameter and the covariate is modelled using cubic splines. The proposed joint Bayesian inference is carried out using adaptive Markov chain Monte Carlo sampling. The deviance information criterion (DIC) is used for selecting the copula family that best approximates the data and for choosing the calibration function. The performances of the estimation and model selection methods are investigated using simulations.

MSC:

62F15 Bayesian inference
62H05 Characterization and structure theory for multivariate probability distributions; copulas
62H20 Measures of association (correlation, canonical correlation, etc.)
65C40 Numerical analysis or methods applied to Markov chains
65C60 Computational problems in statistics (MSC2010)

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