@article{ref1,
title="Inference on dynamic models for non-Gaussian random fields using INLA",
journal="Brazilian journal of probability and statistics",
year="2017",
author="Cortes, R. X. and Martins, T. G. and Prates, M. O. and Silva, B. A.",
volume="31",
number="1",
pages="1-23",
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.<p /> <p>Language: en</p>",
language="en",
issn="0103-0752",
doi="10.1214/15-BJPS300",
url="http://dx.doi.org/10.1214/15-BJPS300"
}