Brazilian Journal of Probability and Statistics

Inference on dynamic models for non-Gaussian random fields using INLA

Abstract

Robust time series analysis is an important subject in statistical modeling. Models based on Gaussian distribution are sensitive to outliers, which may imply in a significant degradation in estimation performance as well as in prediction accuracy. State-space models, also referred as Dynamic Models, is a very useful way to describe the evolution of a time series variable through a structured latent evolution system. Integrated Nested Laplace Approximation (INLA) is a recent approach proposed to perform fast approximate Bayesian inference in Latent Gaussian Models which naturally comprises Dynamic Models. We present how to perform fast and accurate non-Gaussian dynamic modeling with INLA and show how these models can provide a more robust time series analysis when compared with standard dynamic models based on Gaussian distributions. We formalize the framework used to fit complex non-Gaussian space-state models using the R package INLA and illustrate our approach with a simulation study and a Brazilian homicide rate dataset.

Article information

Source
Braz. J. Probab. Stat., Volume 31, Number 1 (2017), 1-23.

Dates
Accepted: September 2015
First available in Project Euclid: 25 January 2017

https://projecteuclid.org/euclid.bjps/1485334822

Digital Object Identifier
doi:10.1214/15-BJPS300

Mathematical Reviews number (MathSciNet)
MR3601658

Zentralblatt MATH identifier
1362.62054

Citation

Cortes, R. X.; Martins, T. G.; Prates, M. O.; Silva, B. A. Inference on dynamic models for non-Gaussian random fields using INLA. Braz. J. Probab. Stat. 31 (2017), no. 1, 1--23. doi:10.1214/15-BJPS300. https://projecteuclid.org/euclid.bjps/1485334822

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