title: Incorporating covariates in skewed functional data models.
abstract: 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.
authors.affiliation: Department of Statistics, North Carolina State University, Raleigh, NC 27695-8203, USA.
authors.forename: Meng
authors.initials: M
authors.lastname: Li
authors.affiliation: Department of Statistics, North Carolina State University, Raleigh, NC 27695-8203, USA ana-maria_staicu@ncsu.edu astaicu@ncsu.edu.
authors.forename: Ana-Maria
authors.initials: AM
authors.lastname: Staicu
authors.affiliation: Department of Statistics, North Carolina State University, Raleigh, NC 27695-8203, USA.
authors.forename: Howard D
authors.initials: HD
authors.lastname: Bondell
mesh_terms: D056808:Biostatistics; D016022:Case-Control Studies; D003198:Computer Simulation; D056324:Diffusion Tensor Imaging / Q000706:statistics & numerical data; D006801:Humans; D015233:Models, Statistical; D009103:Multiple Sclerosis / Q000175:diagnosis; D015999:Multivariate Analysis; D016011:Normal Distribution; D025341:Principal Component Analysis; D012984:Software
keywords: Covariate modeling; Diffusion tensor imaging; Functional principal component analysis; Gaussian copula; Quantile estimation; Skewed function