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Modeling and Prediction of Multiple Correlated Functional Outcomes.

Jiguo CaoKunlaya SoiapornRaymond J CarrollDavid Ruppert
Published in: Journal of agricultural, biological, and environmental statistics (2018)
We propose a copula-based approach for analyzing functional data with correlated multiple functional outcomes exhibiting heterogeneous shape characteristics. To accommodate the possibly large number of parameters due to having several functional outcomes, parameter estimation is performed in two steps: first, the parameters for the marginal distributions are estimated using 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-Loève 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 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. Our proposed methodology is quite general and can be applied to other functional data with multiple outcomes in biology and other fields.
Keyphrases
  • multiple sclerosis
  • electronic health record
  • machine learning
  • mass spectrometry
  • ms ms
  • type diabetes
  • skeletal muscle
  • glycemic control
  • insulin resistance
  • monte carlo
  • artificial intelligence