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dc.contributor.advisorLi, Longhaien_US
dc.contributor.advisorFeng, Cindy.Xinen_US
dc.creatorQiu, Shien_US
dc.date.accessioned2015-04-03T12:00:12Z
dc.date.available2015-04-03T12:00:12Z
dc.date.created2015-03en_US
dc.date.issued2015-04-02en_US
dc.date.submittedMarch 2015en_US
dc.identifier.urihttp://hdl.handle.net/10388/ETD-2015-03-1988en_US
dc.description.abstractThe well-documented problems associated with mapping raw rates of disease have resulted in an increased use of Bayesian hierarchical models to produce maps of "smoothed'' estimates of disease rates. Two statistical problems arise in using Bayesian hierarchical models for disease mapping. The first problem is in comparing goodness of fit of various models, which can be used to test different hypotheses. The second problem is in identifying outliers/divergent regions with unusually high or low residual risk of disease, or those whose disease rates are not well fitted. The results of outlier detection may generate further hypotheses as to what additional covariates might be necessary for explaining the disease. Leave-one-out cross-validatory (LOOCV) model assessment has been used for these two problems. However, actual LOOCV is time-consuming. This thesis introduces two methods, namely iIS and iWAIC, for approximating LOOCV, using only Markov chain samples simulated from a posterior distribution based on a full data set. In iIS and iWAIC, we first integrate the latent variables without reference to holdout observation, then apply IS and WAIC approximations to the integrated predictive density and evaluation function. We apply iIS and iWAIC to two real data sets. Our empirical results show that iIS and iWAIC can provide significantly better estimation of LOOCV model assessment than existing methods including DIC, Importance Sampling, WAIC, posterior checking and Ghosting methods.en_US
dc.language.isoengen_US
dc.subjectcross-validationen_US
dc.subjectdisease mappingen_US
dc.subjectimportance samplingen_US
dc.subjectWAICen_US
dc.titleCross-validatory Model Comparison and Divergent Regions Detection using iIS and iWAIC for Disease Mappingen_US
thesis.degree.departmentMathematics and Statisticsen_US
thesis.degree.disciplineMathematicsen_US
thesis.degree.grantorUniversity of Saskatchewanen_US
thesis.degree.levelMastersen_US
thesis.degree.nameMaster of Science (M.Sc.)en_US
dc.type.materialtexten_US
dc.type.genreThesisen_US
dc.contributor.committeeMemberBickis, Mikelis G.en_US


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