This thesis consists of three papers which investigate marginal models, nonparametric approaches, generalized mixed effects models and variance components estimation in longitudinal data analysis. In the first paper, a new marginal approach is introduced for high-dimensional cell-cycle microarray data with no replicates. There are two kinds of correlation for cell-cycle...
We propose a new classification method for longitudinal data based on a semiparametric approach. Our approach builds a classifier by taking advantage of modeling information between response and covariates for each class, and assigns a new subject to the class with the smallest quadratic distance. This enables one to overcome...
Missing data can lead to biased and inefficient estimation if the missing mechanism is not taken into account in the analysis. In this dissertation we propose two estimators that, under fairly general conditions, are asymptotically unbiased. The first proposed estimator assume the data are missing at random (MAR) and does...