R-INLA

Extending integrated nested Laplace approximation to a class of near-Gaussian latent models. 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.


References in zbMATH (referenced in 12 articles , 1 standard article )

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  1. Andrew Zammit-Mangion, Noel Cressie: FRK: An R Package for Spatial and Spatio-Temporal Prediction with Large Datasets (2017) arXiv
  2. Cortes, R.X.; Martins, T.G.; Prates, M.O.; Silva, B.A.: Inference on dynamic models for non-Gaussian random fields using INLA (2017)
  3. Geppert, Leo N.; Ickstadt, Katja; Munteanu, Alexander; Quedenfeld, Jens; Sohler, Christian: Random projections for Bayesian regression (2017)
  4. Faraway, Julian J.: Extending the linear model with R. Generalized linear, mixed effects and nonparametric regression models. (2016)
  5. Blangiardo, Marta; Cameletti, Michela: Spatial and spatio-temporal Bayesian models with R-INLA (2015)
  6. Bradley, Jonathan R.; Cressie, Noel; Shi, Tao: Comparing and selecting spatial predictors using local criteria (2015)
  7. Casals, Martí; Langohr, Klaus; Carrasco, Josep Lluís; Rönnegård, Lars: Parameter estimation of Poisson generalized linear mixed models based on three different statistical principles: a simulation study (2015)
  8. Ferkingstad, Egil; Rue, Håvard: Improving the INLA approach for approximate Bayesian inference for latent Gaussian models (2015)
  9. Lindgren, Finn: Comments on: “Comparing and selecting spatial predictors using local criteria” (2015)
  10. Datta, Somnath (ed.); Nettleton, Dan (ed.): Statistical analysis of next generation sequencing data (2014)
  11. Martins, Thiago G.; Rue, Håvard: Extending integrated nested Laplace approximation to a class of near-Gaussian latent models (2014)
  12. Wikle, Christopher K.; Hooten, Mevin B.: A general science-based framework for dynamical spatio-temporal models (2010)