Space-varying regression models: Specifications and simulation

Space-varying regression models are generalizations of standard linear models where the regression coefficients are allowed to change in space. The spatial structure is specified by a multivariate extension of pairwise difference priors thus enabling incorporation of neighboring structures and easy sampling schemes. Different sampling schemes are available and may be used in an MCMC algorithm. These schemes are compared in terms of chain autocorrelation and resulting inference. We also discuss different prior specifications that accommodate the spatial structure. Results are illustrated with simulated data and applied to a real dataset.