LAUSR

Most of the existing Bayesian nonparametric models for spatial areal data assume that the neighborhood structures are known, however in practice this assumption may not hold. In this paper, we develop an area-specific stick breaking process for distributions of random effects with the spatially-dependent weights arising from the block averaging of underlying continuous surfaces. We show that this prior, which does not depend on specifying neighboring schemes, is noticeably flexible in effectively capturing heterogeneity in spatial dependency across areas. We illustrate the methodology with a dataset involving expenditure credit of 31 provinces of Iran.