Modeling Victoria's Injection Drug Users

dc.contributor.authorStone, Ryan Alexander
dc.contributor.supervisorCowen, Laura Louise Elizabeth
dc.date.accessioned2013-09-03T20:17:55Z
dc.date.available2014-08-24T11:22:05Z
dc.date.copyright2013en_US
dc.date.issued2013-09-03
dc.degree.departmentDept. of Mathematics and Statisticsen_US
dc.degree.levelMaster of Science M.Sc.en_US
dc.description.abstractThe objective of this thesis is to examine random effect models applied to binary data. I will use classical and Bayesian inference to fit generalized linear mixed models to a specific data set. The data analyzed in this thesis comes from a study examining the injection practices of needle exchange clientele in Victoria, B.C. focusing on their risk networks. First, I will examine the application of social network analysis to the study of injection drug use, focusing on issues of gender, norms, and the problem of hidden populations. Next the focus will be on random effect models, where I will provide some background and a few examples pertaining to generalized linear mixed models (GLMMs). After GLMMs, I will discuss the nature of the injection drug use study and the data which will then be analyzed using a GLMM. Lastly, I will provide a discussion about my results of the GLMM analysis along with a summary of the injection practices of the needle exchange clientele.en_US
dc.description.proquestcode0463en_US
dc.description.scholarlevelGraduateen_US
dc.identifier.urihttp://hdl.handle.net/1828/4899
dc.languageEnglisheng
dc.language.isoenen_US
dc.rights.tempAvailable to the World Wide Weben_US
dc.subjectStatisticsen_US
dc.subjectGLMMen_US
dc.subjectGLMen_US
dc.subjectBayesianen_US
dc.subjectNetworksen_US
dc.subjectEgocentricen_US
dc.subjectSharingen_US
dc.subjectHarm reductionen_US
dc.subjectNormsen_US
dc.titleModeling Victoria's Injection Drug Usersen_US
dc.typeThesisen_US

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