Sajobi, Tolulope T.Wiebe, SamuelBrobbey, Anita2021-01-132021-01-132021-01-08Brobbey, A. (2021). Classification Models for Multivariate Non-normal Repeated Measures Data (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.http://hdl.handle.net/1880/112972Multivariate repeated measures data, in which multiple outcomes are repeatedly measured at two or more occasions, are commonly collected in several disciplines (e.g., medicine, ecology, environmental sciences), where investigators seek to discriminate between population groups or make predictions based on changes in multiple correlated outcomes over time. Repeated measures discriminant analysis have been developed and applied to address these research questions. These classification models, which have been mostly developed based on growth curve models, covariance pattern models, and mixed-effects models, are advantageous in that they can account for complex correlation structures in multivariate repeated measures data (e.g., within-outcome and between-outcome correlations) to improve their predictive accuracy. However, they largely rely on the assumption of multivariate normality, which is rarely satisfied in multivariate repeated measures data. To our knowledge, there has been limited investigation of the behavior of these existing models in multivariate non-normal repeated measures data. The overarching goal of this research was to develop robust repeated measures discriminant analysis classifiers for multivariate non-normal repeated measures data. Specifically, we developed repeated measures discriminant analysis based on maximum trimmed likelihood estimators (MTLE) and generalized estimating equations (GEE) estimators and examine their accuracy in comparison to classifiers based on maximum likelihood estimation (MLE) using Monte Carlo methods. The simulation conditions examined, included population distribution, sample size, covariance structure (between-outcomes and within-outcome), covariance heterogeneity, repeated number of occasions, and number of outcome variables. The Monte Carlo study results indicated that the proposed methods increased overall mean classification accuracy by 2% - 15% in multivariate non-normal repeated measures data compared to repeated measures discriminant analysis based on MLE under most scenarios. Data from two cohort studies were used to illustrate the implementation of the proposed repeated measures discriminant analysis methods. The outcomes of this research includes novel multivariate classifiers for predicting group membership in multivariate normal and non-normal repeated measures data. This research contributes to the advancement of statistical science on methods for analyzing multivariate repeated measures data.engUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.discriminant analysismultivariate repeated measures dataclassificationmultivariate non-normal distributionrobust methodsEducation--HealthEducation--MathematicsStatisticsdoctoral thesis10.11575/PRISM/38553