A semiparametric Bayesian approach to joint mean and variance models

We propose a fully Bayesian inference for semiparametric joint mean and variance models on the basis of B-spline approximations of nonparametric components. An efficient MCMC method which combines Gibbs sampler and Metropolis–Hastings algorithm is suggested for the inference, and the methodology is illustrated through a simulation study and a real example.