Incorporating covariates in skewed functional data models

Biostatistics. 2015 Jul;16(3):413-26. doi: 10.1093/biostatistics/kxu055. Epub 2014 Dec 19.


We introduce a class of covariate-adjusted skewed functional models (cSFM) designed for functional data exhibiting location-dependent marginal distributions. We propose a semi-parametric copula model for the pointwise marginal distributions, which are allowed to depend on covariates, and the functional dependence, which is assumed covariate invariant. The proposed cSFM framework provides a unifying platform for pointwise quantile estimation and trajectory prediction. We consider a computationally feasible procedure that handles densely as well as sparsely observed functional data. The methods are examined numerically using simulations and is applied to a new tractography study of multiple sclerosis. Furthermore, the methodology is implemented in the R package cSFM, which is publicly available on CRAN.

Keywords: Covariate modeling; Diffusion tensor imaging; Functional principal component analysis; Gaussian copula; Quantile estimation; Skewed function.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Biostatistics
  • Case-Control Studies
  • Computer Simulation
  • Diffusion Tensor Imaging / statistics & numerical data
  • Humans
  • Models, Statistical*
  • Multiple Sclerosis / diagnosis
  • Multivariate Analysis*
  • Normal Distribution
  • Principal Component Analysis
  • Software