Bayesian hierarchical models for spatial count data with application to fire frequency in British Columbia

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dc.contributor.authorLi, Hong
dc.contributor.supervisorNathoo, Farouk
dc.date.accessioned2008-12-16T00:54:01Z
dc.date.available2008-12-16T00:54:01Z
dc.date.copyright2008en_US
dc.date.issued2008-12-16T00:54:01Z
dc.degree.departmentDept. of Mathematics and Statisticsen_US
dc.degree.levelMaster of Science M.Sc.en_US
dc.description.abstractThis 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.en_US
dc.identifier.urihttp://hdl.handle.net/1828/1293
dc.languageEnglisheng
dc.language.isoenen_US
dc.rightsAvailable to the World Wide Weben_US
dc.subjectBayesianen_US
dc.subjectMarkov Chain Monte Carloen_US
dc.subjectNegative Binomialen_US
dc.subjectGeneralized Linear Modelen_US
dc.subject.lcshUVic Subject Index::Sciences and Engineering::Mathematics::Mathematical statisticsen_US
dc.titleBayesian hierarchical models for spatial count data with application to fire frequency in British Columbiaen_US
dc.typeThesisen_US

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