Open Conference Systems, 50th Scientific meeting of the Italian Statistical Society

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Assessment of the INLA approach on gerarchic bayesian models for the spatial disease distribution: a real data application
Paolo Girardi, Emanuela Bovo, Carmen Stocco, Susanna Baracco, Alberto Rosano, Daniele Monetti, Silvia Rizzato, Sara Zamberlan, Enrico Chinellato, Ugo Fedeli, Massimo Rugge

Last modified: 2018-05-10


The use of Laplace approximation methods as INLA (Integrated Nested Laplace Approximation) is being widely used in Bayesian inference, especially in spatial risk regression as the Besag-York-Molliè (BYM) model. INLA is time saving compared to Monte Carlo simulations based on Markov Chains (MCMC), but it produces differences in estimates \cite{Smedt2015,Carroll2015}. In order to perform a comparison, we consider data from the Veneto Cancer Registry. INLA returns estimates comparable to MCMC, but it appears sensitive to the a-priori distribution, while MCMC estimation seems to be more robust.  INLA is a fast and efficient method. However, particular care must be paid to the choice of the parameter relating to the a-priori distribution in both methods.


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