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Hierarchical dynamic modeling of outbreaks of mountain pine beetle using partial differential equations
Author(s) -
Zheng Yanbing,
Aukema Brian H.
Publication year - 2010
Publication title -
environmetrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.68
H-Index - 58
eISSN - 1099-095X
pISSN - 1180-4009
DOI - 10.1002/env.1058
Subject(s) - markov chain monte carlo , bayesian inference , spatial dependence , bayesian probability , approximate bayesian computation , inference , markov chain , monte carlo method , computer science , statistical physics , computation , statistics , econometrics , mathematics , algorithm , artificial intelligence , physics
In this article, we develop spatial—temporal generalized linear mixed models for spatial—temporal binary data observed on a spatial lattice and repeatedly over discrete time points. To account for spatial and temporal dependence, we introduce a spatial—temporal random effect in the link function and model by a diffusion—convection dynamic model. We propose a Bayesian hierarchical model for statistical inference and devise Markov chain Monte Carlo algorithms for computation. We illustrate the methodology by an example of outbreaks of mountain pine beetle on the Chilcotin Plateau of British Columbia, Canada. We examine the effect of environmental factors while accounting for the potential spatial and temporal dependence. Copyright © 2010 John Wiley & Sons, Ltd.