Bayesian hierarchical models for spatial count data with application to fire frequency in British Columbia
Date
2008-12-16T00:54:01Z
Authors
Li, Hong
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Abstract
This thesis develops hierarchical spatial models for the analysis of correlated and
overdispersed count data based on the negative binomial distribution. Model development
is motivated by a large scale study of fire frequency in British Columbia,
conducted by the Pacific Forestry Service. Specific to our analysis, the main focus
lies in examining the interaction between wildfire and forest insect outbreaks. In
particular, we wish to relate the frequency of wildfire to the severity of mountain
pine beetle (MPB) outbreaks in the province. There is a widespread belief that forest
insect outbreaks lead to an increased frequency of wildfires; however, empirical evidence
to date has been limited and thus a greater understanding of the association is
required. This is critically important as British Columbia is currently experiencing
a historically unprecedented MPB outbreak. We specify regression models for fire
frequency incorporating random effects in a generalized linear mixed modeling framework.
Within such a framework, both spatial correlation and extra-Poisson variation
can be accommodated through random effects that are incorporated into the linear
predictor of a generalized linear model. We consider a range of models, and conduct
model selection and inference within the Bayesian framework with implementation
based on Markov Chain Monte Carlo.
Description
Keywords
Bayesian, Markov Chain Monte Carlo, Negative Binomial, Generalized Linear Model