Online Program Home
My Program

Abstract Details

Activity Number: 52
Type: Invited
Date/Time: Sunday, July 31, 2016 : 4:00 PM to 5:50 PM
Sponsor: ENAR
Abstract #318407
Title: Copula Random Field with Application to Longitudinal Neuroimaging Data Analysis
Author(s): Peter X. K. Song* and Jian Kang
Companies: University of Michigan and University of Michigan
Keywords: Gaussian copula ; random field ; spatial data ; composite likelihood

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.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2016 program

Copyright © American Statistical Association