Modeling and Prediction of Multiple Correlated Functional Outcomes.

ABSTRACT: 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.

SUBMITTER: Cao J 

PROVIDER: S-EPMC6447061 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature