Spatial disease mapping using directed acyclic graph auto-regressive (DAGAR) models.

ABSTRACT: Hierarchical models for regionally aggregated disease incidence data commonly involve region specific latent random effects that are modeled jointly as having a multivariate Gaussian distribution. The covariance or precision matrix incorporates the spatial dependence between the regions. Common choices for the precision matrix include the widely used ICAR model, which is singular, and its nonsingular extension which lacks interpretability. We propose a new parametric model for the precision matrix based on a directed acyclic graph (DAG) representation of the spatial dependence. Our model guarantees positive definiteness and, hence, in addition to being a valid prior for regional spatially correlated random effects, can also directly model the outcome from dependent data like images and net

SUBMITTER: Datta A 

PROVIDER: S-EPMC8046356 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature