Analysis of Serially Dependent Multivariate Longitudinal Non-Gaussian Continuous Data
Date
2015-09-29
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Abstract
Serially dependent multivariate longitudinal non-Gaussian outcome data are commonly encountered
in many fields of study, especially in biomedical sciences, finance, and so on. However,
flexible methodologies for joint analysis of these outcomes are not well developed. Recently, Wu
and de Leon (2014) and Withanage and de Leon (2015) introduced the class of Gaussian copula
mixed models (GCMMs) for joint analysis of non-Gaussian outcomes. We adapt and extend the
GCMM to settings that involve conditional as well as serial dependencies among longitudinal observations
on the same or on different outcomes. We investigate the impact of failing to account
for these dependencies via simulations. We illustrate our methodology on two datasets: one on
data obtained from primary biliary cirrhosis patients, and the other on data from the Iowa Youth
and Families Project.
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Statistics
Citation
Yeasmin, F. (2015). Analysis of Serially Dependent Multivariate Longitudinal Non-Gaussian Continuous Data (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/24822