Bayesian Inference for Generalized Additive Regression based on Dynamic Models

We present a general approach for Bayesian inference via Markov chain Monte Carlo (MCMC) simulation in generalized additive, semiparametric and mixed models. It is particularly appropriate for discrete and other fundamentally non-Gaussian responses, where Gibbs sampling techniques developed for Gaussian models cannot be applied. We use the close relation between nonparametric regression and dynamic or state space models to develop posterior sampling procedures that are based on recent Metropolis-Hasting algorithms for dynamic generalized linear models. We illustrate the approach with applications to credit scoring and unemployment duration