Direct fitting of dynamic models using integrated nested Laplace approximations - INLA

Ramiro Ruiz-Cárdenas*, Elias T. Krainski, Haavard Rue

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

37 Scopus citations

Abstract

Inference in state-space models usually relies on recursive forms for filtering and smoothing of the state vectors regarding the temporal structure of the observations, an assumption that is, from our view point, unnecessary if the dataset is fixed, that is, completely available before analysis. In this paper, we propose a computational framework to perform approximate full Bayesian inference in linear and generalized dynamic linear models based on the Integrated Nested Laplace Approximation (INLA) approach. The proposed framework directly approximates the posterior marginals of interest disregarding the assumption of recursive updating/estimation of the states and hyperparameters in the case of fixed datasets and, therefore, enable us to do fully Bayesian analysis of complex state-space models more easily and in a short computational time. The proposed framework overcomes some limitations of current tools in the dynamic modeling literature and is vastly illustrated with a series of simulated as well as well known real-life examples from the literature, including realistically complex models with correlated error structures and models with more than one state vector, being mutually dependent on each other. R code is available online for all the examples presented.

Original languageEnglish (US)
Pages (from-to)1808-1828
Number of pages21
JournalComputational Statistics and Data Analysis
Volume56
Issue number6
DOIs
StatePublished - Jun 1 2012

Keywords

  • Approximate Bayesian inference
  • Augmented model
  • Laplace approximation
  • Spatio-temporal dynamic models
  • State-space models

ASJC Scopus subject areas

  • Statistics and Probability
  • Computational Mathematics
  • Computational Theory and Mathematics
  • Applied Mathematics

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