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Activity Number: 613
Type: Invited
Date/Time: Thursday, August 8, 2013 : 8:30 AM to 10:20 AM
Sponsor: Section on Nonparametric Statistics
Abstract - #307197
Title: Modeling Multiple Correlated Functional Outcomes with Spatially Heterogeneous Shape Characteristics
Author(s): David Ruppert*+ and Kunlaya Soiaporn and Raymond J. Carroll
Companies: Cornell University and Cornell University and Texas A&M University
Keywords: Multiple Outcome FDA ; Non-Gaussian ; DTI
Abstract:

We propose a copula-based approach for analyzing functional data with multiple outcomes exhibiting spatially heterogeneous shape characteristics. To accommodate the possibly large number of parameters in multiple outcome data, parameter estimation is performed in two steps: first, the parameters for the marginal distributions are estimated using a parametric family such as the skew t family, and then the dependence structure both within and across outcomes is estimated using a Gaussian copula. We develop an estimation algorithm for the dependence parameters based on the Karhunen-Loeve expansion and an EM algorithm that significantly reduces the dimension of the problem and is computationally efficient. We also demonstrate prediction of an unknown outcome when the other outcomes are known. We apply our methodology to diffusion tensor imaging (DTI) data for multiple sclerosis (MS) patients with three outcomes, and identify differences in both the marginal distributions and the dependence structure between the MS and control groups. ROC curves show that the cross-correlations between DTI outcomes are predictive of MS status.


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