Evaluating the impact of a small number of areas on spatial estimation.

ABSTRACT:

Background

There is an expanding literature on different representations of spatial random effects for different types of spatial correlation structure within the conditional autoregressive class of priors for Bayesian spatial models. However, little is known about the impact of these different priors when the number of areas is small. This paper aimed to investigate this problem both in the context of a case study of spatial analysis of dengue fever and more generally through a simulation study.

Methods

Both the simulation study and the case study considered count data aggregated to a small area level in a region. Five different conditional autoregressive priors for a simple Bayesian Poisson model were considered: independent, Besag-York-Mollié, Leroux, and two variants of

SUBMITTER: Aswi A 

PROVIDER: S-EPMC7519538 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature