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 30 articles , 2 standard articles )

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  1. Cowles, Mary Kathryn; Bonett, Stephen; Seedorff, Michael: Independent sampling for Bayesian normal conditional autoregressive models with OpenCL acceleration (2018)
  2. Duncan Lee; Alastair Rushworth; Gary Napier: Spatio-Temporal Areal Unit Modeling in R with Conditional Autoregressive Priors Using the CARBayesST Package (2018)
  3. Jingyi Guo; Andrea Riebler: meta4diag: Bayesian Bivariate Meta-Analysis of Diagnostic Test Studies for Routine Practice (2018)
  4. Jing Zhao; Jian’an Luan; Peter Congdon: Bayesian Linear Mixed Models with Polygenic Effects (2018)
  5. Wagner Bonat: Multiple Response Variables Regression Models in R: The mcglm Package (2018)
  6. Wang, Xiaofeng; Yue, Yu Ryan; Faraway, Julian J.: Bayesian regression modeling with INLA (2018)
  7. Andrew Zammit-Mangion, Noel Cressie: FRK: An R Package for Spatial and Spatio-Temporal Prediction with Large Datasets (2017) arXiv
  8. Cortes, R. X.; Martins, T. G.; Prates, M. O.; Silva, B. A.: Inference on dynamic models for non-Gaussian random fields using INLA (2017)
  9. Dunlop, Matthew M.; Iglesias, Marco A.; Stuart, Andrew M.: Hierarchical Bayesian level set inversion (2017)
  10. Geppert, Leo N.; Ickstadt, Katja; Munteanu, Alexander; Quedenfeld, Jens; Sohler, Christian: Random projections for Bayesian regression (2017)
  11. Jouni Helske: KFAS: Exponential Family State Space Models in R (2017)
  12. Opitz, Thomas: Latent Gaussian modeling and INLA: a review with focus on space-time applications (2017)
  13. RESSTE Network et al.: Analyzing spatio-temporal data with R: everything you always wanted to know -- but were afraid to ask (2017)
  14. Tobias Liboschik; Konstantinos Fokianos; Roland Fried: tscount: An R Package for Analysis of Count Time Series Following Generalized Linear Models (2017)
  15. Bradley, Jonathan R.; Cressie, Noel; Shi, Tao: A comparison of spatial predictors when datasets could be very large (2016)
  16. Faraway, Julian J.: Extending the linear model with R. Generalized linear, mixed effects and nonparametric regression models. (2016)
  17. Blangiardo, Marta; Cameletti, Michela: Spatial and spatio-temporal Bayesian models with R-INLA (2015)
  18. Bradley, Jonathan R.; Cressie, Noel; Shi, Tao: Comparing and selecting spatial predictors using local criteria (2015)
  19. 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)
  20. Edzer Pebesma; Roger Bivand; Paulo Ribeiro: Software for Spatial Statistics (2015)

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