Multivariate Spatial Nonparametric Modelling via Kernel

In this paper we develop a nonparametric multivariate spatial model that avoids specifying a Gaussian distribution for spatial random effects. Our nonparametric model extends the stick-breaking (SB) prior of Sethuraman (1994), which is frequently used in Bayesian mod-elling to capture uncertainty in the parametric form of an outcome. The stick-breaking prior is extended here to the spatial setting by assigning each location a different, unknown dis-tribution, and smoothing the distributions in space with a series of space-dependent kernel functions that have a space-varying bandwidth parameter. This results in a flexible non-stationary spatial model, as different kernel functions lead to different relationships betwee