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Fitting Generalized Zero-Inflated Poisson Regression Mixture Models to Bacteria Microbiome Data

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dc.contributor.advisor Feng, Zeny
dc.contributor.author Chen, Siyu
dc.date.accessioned 2018-04-20T15:49:24Z
dc.date.available 2018-04-20T15:49:24Z
dc.date.copyright 2018-04
dc.date.created 2018-04-13
dc.date.issued 2018-04-20
dc.identifier.uri http://hdl.handle.net/10214/12612
dc.description.abstract Gut microbial dysbiosis contributes to the risk of colorectal cancer, thus it is important to study the gut mucosal microbiome. Gut bacteria microbiome data has the features of excess zeros and overdispersion that restrict the use of fitting traditional Poisson regression models to this kind of count data. We propose the use of the generalized zero-inflated Poisson (GZIP) regression mixture model for analyzing such data. When fitting a mixture model, we need to specify the number of components in a given population. However, the number of components is unknown. In this thesis, the Bayesian information criterion (BIC) is used to identify a preferred model with a pre-specified number of components. The EM algorithm is used to estimate parameters and the performance of the models is assessed by simulation studies. The GZIP mixture model is applied to gut bacteria microbiome data from a colorectal cancer study. We only consider the carcinoma and healthy groups as a health state covariate and select the best fitted GZIP model to each bacteria genus from models of two, three, or four components. Some special cases where the proposed methods failed to be applied are also discussed. en_US
dc.language.iso en en_US
dc.subject GZIP regression mixture model en_US
dc.subject bacteria microbiome data en_US
dc.title Fitting Generalized Zero-Inflated Poisson Regression Mixture Models to Bacteria Microbiome Data en_US
dc.type Thesis en_US
dc.degree.programme Mathematics and Statistics en_US
dc.degree.name Master of Science en_US
dc.degree.department Department of Mathematics and Statistics en_US
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