TY - CHAP
T1 - Using INLA to Estimate a Highly Dimensional Spatial Model for Forest Fires in Portugal
AU - Natário, Isabel
AU - Oliveira, Maria Manuela
AU - Marques, Susete
PY - 2014/4/3
Y1 - 2014/4/3
N2 - Within the context of accessing the risk of forest fires, Amaral-Turkman et al. (Environ. Ecol. Stat. 18:601–617, 2011) have proposed a spatio-temporal hierarchical approach which jointly models the fire ignition probability and the fire’s size, in a Bayesian framework. This is recovered and applied to Portuguese forest fires data, with some necessary modifications in what concerns the format of the data (not available in a regular lattice over the territory) and also because of the estimation complications that arise due to the high dimensionality of the neighbouring structure involved. To address the latter, as it compromises the estimation via Markov Chain Monte Carlo (MCMC) methods, and having the model be recognized as a latent Gaussian model, it was chosen to do the Bayesian estimation also using an Integrated Nested Laplace Approximation approach, with real computational advantages. Corresponding methodologies and results are described and compared.
AB - Within the context of accessing the risk of forest fires, Amaral-Turkman et al. (Environ. Ecol. Stat. 18:601–617, 2011) have proposed a spatio-temporal hierarchical approach which jointly models the fire ignition probability and the fire’s size, in a Bayesian framework. This is recovered and applied to Portuguese forest fires data, with some necessary modifications in what concerns the format of the data (not available in a regular lattice over the territory) and also because of the estimation complications that arise due to the high dimensionality of the neighbouring structure involved. To address the latter, as it compromises the estimation via Markov Chain Monte Carlo (MCMC) methods, and having the model be recognized as a latent Gaussian model, it was chosen to do the Bayesian estimation also using an Integrated Nested Laplace Approximation approach, with real computational advantages. Corresponding methodologies and results are described and compared.
KW - Forest Fire
KW - Markov Chain Monte Carlo
KW - Markov Chain Monte Carlo Method
KW - Deviance Information Criterion
UR - http://www.scopus.com/record/display.uri?eid=2-s2.0-85060311342&origin=resultslist&sort=plf-f&src=s&st1
U2 - 10.1007/978-3-319-05323-3_23
DO - 10.1007/978-3-319-05323-3_23
M3 - Chapter
SN - 978-3-319-05322-6
VL - Part IV
T3 - Studies in Theoretical and Applied Statistics
SP - 239
EP - 247
BT - New Advances in Statistical Modeling and Applications
A2 - Pacheco, António
A2 - Santos, Rui
A2 - Oliveira, do Rosário Maria
A2 - Paulino, Daniel Carlos
PB - Springer International Publishing
CY - Cham
ER -