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

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  • Thiago G. Martins
  • Håvard Rue

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.

Suggested Citation

  • Thiago G. Martins & Håvard Rue, 2014. "Extending Integrated Nested Laplace Approximation to a Class of Near-Gaussian Latent Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 41(4), pages 893-912, December.
    • Handle: RePEc:bla:scjsta:v:41:y:2014:i:4:p:893-912
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    File URL: http://hdl.handle.net/10.1111/sjos.12073
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    References listed on IDEAS

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    1. Martins, Thiago G. & Simpson, Daniel & Lindgren, Finn & Rue, Håvard, 2013. "Bayesian computing with INLA: New features," Computational Statistics & Data Analysis, Elsevier, vol. 67(C), pages 68-83.
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    1. Janet Niekerk & Haakon Bakka & Håvard Rue, 2023. "Stable Non-Linear Generalized Bayesian Joint Models for Survival-Longitudinal Data," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(1), pages 102-128, February.

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