A new Bayesian nonparametric framework for multivariate conditional copula regression with varying coefficients uses adaptive spline marginals and probit stick-breaking infinite Gaussian copula mixtures to flexibly capture covariate-dependent dependencies in mixed outcomes.
Therefore, we obtain (S13) by Pinsker’s inequality
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Bayesian Nonparametric Modeling for Multivariate Conditional Copula Regression with Varying Coefficients
A new Bayesian nonparametric framework for multivariate conditional copula regression with varying coefficients uses adaptive spline marginals and probit stick-breaking infinite Gaussian copula mixtures to flexibly capture covariate-dependent dependencies in mixed outcomes.