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Estimation and Clustering on Longitudinal Data Using Penalized Spline Models


It is important to identify and characterize associations between microorganisms and human processes because they suggest possible involvements of health or disease processes and the interaction of organisms. My dissertation is motivated by a type of longitudinal

data in which the number of microorganisms in several mice are measured over time under different operational taxonomic units. This study focuses on such type of data using clustering on linear mixed models and generalized linear mixed models with the effects of operational taxonomic units and subjects

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