Abstract:
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Motivated by the needs of analyzing massive longitudinal imaging data, we present an extension of GeoCopula proposed by Bai et al. (2014). This new model, termed as imageCopula, helps us to address multilevel spatial-temporal dependencies arising from longitudinal imaging data. We propose an efficient composite likelihood approach by constructing joint composite estimating equations (JCEE) and develop computationally feasible algorithm to solve the JCEE. We show that the computation is scalable to large-scale imaging data. We conduct several simulation studies to evaluate the performance of the proposed models and estimation methods. We apply the imageCopula to analyze a longitudinal PET data set from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study.
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