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Title of Journal: Comput Stat

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Abbravation: Computational Statistics

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Springer-Verlag

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10.1002/cne.902290310

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1613-9658

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A primer on disease mapping and ecological regress

Authors: Birgit Schrödle Leonhard Held
Publish Date: 2010/08/13
Volume: 26, Issue: 2, Pages: 241-258
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Abstract

Spatial and spatiotemporal disease mapping models are widely used for the analysis of registry data and usually formulated in a hierarchical Bayesian framework Explanatory variables can be included by a socalled ecological regression It is possible to assume both a linear and a nonparametric association between disease incidence and the explanatory variable Integrated nested Laplace approximations INLA can be used as a tool for Bayesian inference INLA is a promising alternative to Markov chain Monte Carlo MCMC methods which provides very accurate results within short computational time It is shown in this paper how parameter estimates for wellknown spatial and spatiotemporal models can be obtained by running INLA directly in textttR using the package textttINLA Selected textttR code is shown An emphasis is given to the inclusion of an explanatory variable Cases of Coxiellosis among Swiss cows from 2005 to 2008 are used for illustration The number of stillborn calves is included as timevarying covariate Additionally various aspects of INLA such as model choice criteria computer time accuracy of the results and usability of the textttR package are discussed

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  3. GEVA: geometric variability-based approaches for identifying patterns in data
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  17. The Monte Carlo EM method for estimating multinomial probit latent variable models
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