Improving the INLA approach for approximate Bayesian inference for latent Gaussian models

Egil Ferkingstad, Haavard Rue

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

We introduce a new copula-based correction for generalized linear mixed models (GLMMs) within the integrated nested Laplace approximation (INLA) approach for approximate Bayesian inference for latent Gaussian models. While INLA is usually very accurate, some (rather extreme) cases of GLMMs with e.g. binomial or Poisson data have been seen to be problematic. Inaccuracies can occur when there is a very low degree of smoothing or “borrowing strength” within the model, and we have therefore developed a correction aiming to push the boundaries of the applicability of INLA. Our new correction has been implemented as part of the R-INLA package, and adds only negligible computational cost. Empirical evaluations on both real and simulated data indicate that the method works well.

Original languageEnglish (US)
Pages (from-to)2706-2731
Number of pages26
JournalElectronic Journal of Statistics
Volume9
Issue number2
DOIs
StatePublished - Aug 19 2015

Keywords

  • Bayesian computation
  • Copulas
  • Generalized linear mixed models
  • Integrated nested laplace approximation
  • Latent Gaussian models

ASJC Scopus subject areas

  • Statistics and Probability

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