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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/21025

Title: Comparing INLA and OpenBUGS for hierarchical Poisson modeling in disease mapping.
Authors: Carroll, R.
Lawson, A.B.
Faes, C.
Kirby, R.S.
Aregay, Mehreteab
Watjou, K.
Issue Date: 2015
Citation: Spatial and spatio-temporal epidemiology (Print), 14-15, p. 45-54
Abstract: The recently developed R package INLA (Integrated Nested Laplace Approximation) is becoming a more widely used package for Bayesian inference. The INLA software has been promoted as a fast alternative to MCMC for disease mapping applications. Here, we compare the INLA package to the MCMC approach by way of the BRugs package in R, which calls OpenBUGS. We focus on the Poisson data model commonly used for disease mapping. Ultimately, INLA is a computationally efficient way of implementing Bayesian methods and returns nearly identical estimates for fixed parameters in comparison to OpenBUGS, but falls short in recovering the true estimates for the random effects, their precisions, and model goodness of fit measures under the default settings. We assumed default settings for ground truth parameters, and through altering these default settings in our simulation study, we were able to recover estimates comparable to those produced in OpenBUGS under the same assumptions.
URI: http://hdl.handle.net/1942/21025
DOI: 10.1016/j.sste.2015.08.001
ISSN: 1877-5845
Category: A1
Type: Journal Contribution
Validation: vabb, 2017
Appears in Collections: Research publications

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