Spatial smoothing in Bayesian models: a comparison of weights matrix specifications and their impact on inference.

ABSTRACT:

Background

When analysing spatial data, it is important to account for spatial autocorrelation. In Bayesian statistics, spatial autocorrelation is commonly modelled by the intrinsic conditional autoregressive prior distribution. At the heart of this model is a spatial weights matrix which controls the behaviour and degree of spatial smoothing. The purpose of this study is to review the main specifications of the spatial weights matrix found in the literature, and together with some new and less common specifications, compare the effect that they have on smoothing and model performance.

Methods

The popular BYM model is described, and a simple solution for addressing the identifiability issue among the spatial random effects is provided. Seventeen different definitions of the

SUBMITTER: Duncan EW 

PROVIDER: S-EPMC5732501 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature