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Antedependence Models For Skewed Continuous Longitudinal Data, Shu-Ching Chang
Antedependence Models For Skewed Continuous Longitudinal Data, Shu-Ching Chang
Shu-Ching Chang, PhD, MS
This thesis explores the problems of fitting antedependence (AD) models and partial antecorrelation (PAC) models to continuous non-Gaussian longitudinal data. AD models impose certain conditional independence relations among the measurements within each subject, while PAC models characterize the partial correlation relations. The models are parsimonious and useful for data exhibiting time-dependent correlations.
Since the relation of conditional independence among variables is rather restrictive, we first consider an autoregressively characterized PAC model with independent asymmetric Laplace (ALD) innovations and prove that this model is an AD model. The ALD distribution previously has been applied to quantile regression and has shown promise …