Bayesian Inference for Generalized Additive Regression based on Dynamic Models

Fahrmeir, Ludwig and Lang, S. (1998): Bayesian Inference for Generalized Additive Regression based on Dynamic Models. Collaborative Research Center 386, Discussion Paper 134

Abstract

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.

• Item Type: Paper
• Faculties: Mathematics, Computer Science and Statistics > Statistics > Collaborative Research Center 386; Special Research Fields > Special Research Field 386
• Subjects: 500 Science > 510 Mathematics
• Language: English