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Bagging Classification Trees for Longitudinal Data

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Title: Bagging Classification Trees for Longitudinal Data
Author: Alattas, Ali
Department: Department of Mathematics and Statistics
Program: Mathematics and Statistics
Advisor: Horrocks, Julie
Abstract: Many studies handle binary longitudinal data by model-based classifiers, although their assumptions are often unsatisfied with real data. In contrast, tree-based classifiers are free of distributional assumptions, and several of these classifiers use bootstrap samples to construct multiple trees and combine them, in order to reduce prediction error. For paired data, Adler et al. (2011a) compared two types of bagging, namely subject-based strategies and observation-based strategies (one, all). In this thesis, we extended these strategies to longitudinal data. To evaluate the performance of the strategies, we compared them twice. Subject-based-strategies are classified by bagging, and the observation-based strategies are classified either by bagged or a single tree. We found the random (bootstrap) to be the best strategy whether the covariates are time-fixed or time-varying. We illustrated the five strategies on a subset of a well-known dataset on mother’s stress and children’s morbidity.
Date: 2018-12-01
Rights: Attribution-NoDerivatives 4.0 International
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Attribution-NoDerivatives 4.0 International Except where otherwise noted, this item's license is described as Attribution-NoDerivatives 4.0 International