A bayesian approach to nonparametric bivariate regression

Abstract

This article outlines a general Bayesian approach to estimating a bivariate regression function in a nonparametric manner. It models the function using a bivariate regression spline basis with many terms. Binary indicator variables corresponding to these terms are introduced to explicitly model the uncertainty of whether or not the terms provide a significant contribution to the regression. The regression function is estimated using an estimate of its posterior mean, smoothing over the distribution of these binary indicator variables. To make the computations tractable, all estimates are obtained using Markov chain Monte Carlo sampling. Extensive simulated comparisons are provided that demon..