Extending Integrated Nested Laplace Approximation to a Class of Near-Gaussian Latent Models

Thiago G. Martins and Håvard Rue

Scandinavian Journal of Statistics, 2014, vol. 41, issue 4, 893-912

Abstract: type="main" xml:id="sjos12073-abs-0001"> This work extends the integrated nested Laplace approximation (INLA) method to latent models outside the scope of latent Gaussian models, where independent components of the latent field can have a near-Gaussian distribution. The proposed methodology is an essential component of a bigger project that aims to extend the R package INLA in order to allow the user to add flexibility and challenge the Gaussian assumptions of some of the model components in a straightforward and intuitive way. Our approach is applied to two examples, and the results are compared with that obtained by Markov chain Monte Carlo, showing similar accuracy with only a small fraction of computational time. Implementation of the proposed extension is available in the R-INLA package.

Date: 2014
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