University of Warwick - Department of Statistics
In this paper we discuss the problem of Bayesian fully nonparametric regression. The paper is concerned with two issues: 1) a new construction of priors for nonparametric regression is discussed and a specific prior, the Dirichlet Process Regression Smoother, is proposed, and 2) we consider the problem of centring a dependent nonparametric prior over a class of regression models and propose fully nonparametric regression models with flexible location structures. Computational methods are developed for all models described. Results are presented for simulated and actual data examples.
Keywords: Nonlinear regression; Nonparametric regression; Model centring; Stick-breaking prior
This is joint work with Jim Griffin (University of Kent)