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A Comparison of Statistical Models and Deep Learning for Data with Binary Response and Longitudinal Covariates

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dc.contributor.advisor Horrocks, Julie
dc.contributor.author Chen, Zhikang
dc.date.accessioned 2021-05-17T18:47:41Z
dc.date.available 2021-05-17T18:47:41Z
dc.date.copyright 2021-05-03
dc.date.created 2021-04-30
dc.identifier.uri https://hdl.handle.net/10214/25745
dc.description.abstract In statistics, longitudinal data refers to data in which the response variable and explanatory variables are measured several times for each subject. However, in the machine learning literature, longitudinal data can also refer to data in which only the explanatory variables are repeatedly measured, but not the response variable. This thesis compared two statistical models - the baseline logistic regression and the two-stage joint model, and two neural network approaches - the feed-forward neural network and the recurrent neural network with long short-term memory, in terms of the prediction sensitivity, specificity, area under the receiver operating characteristic curve, and Brier score. Data analysis was conducted using data from two clinical trials and a simulation study was also conducted. For the datasets generated and studied in this thesis, the neural network approaches show no advantages compared to the other statistical methods. en_US
dc.language.iso en en_US
dc.publisher University of Guelph en_US
dc.rights Attribution 4.0 International *
dc.rights.uri http://creativecommons.org/licenses/by/4.0/ *
dc.subject Deep Learning en_US
dc.subject FNN en_US
dc.subject RNN en_US
dc.subject Joint Model en_US
dc.subject Two-stage Model en_US
dc.subject Longitudinal en_US
dc.title A Comparison of Statistical Models and Deep Learning for Data with Binary Response and Longitudinal Covariates 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
dc.degree.grantor University of Guelph en_US


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