title: Longitudinal Functional Data Analysis.
abstract: We consider dependent functional data that are correlated because of a longitudinal-based design: each subject is observed at repeated times and at each time a functional observation (curve) is recorded. We propose a novel parsimonious modeling framework for repeatedly observed functional observations that allows to extract low dimensional features. The proposed methodology accounts for the longitudinal design, is designed to study the dynamic behavior of the underlying process, allows prediction of full future trajectory, and is computationally fast. Theoretical properties of this framework are studied and numerical investigations confirm excellent behavior in finite samples. The proposed method is motivated by and applied to a diffusion tensor imaging study of multiple sclerosis.
authors.affiliation: Department of Statistics, North Carolina State University, Raleigh, NC 27695-8203, USA.
authors.forename: So Young
authors.initials: SY
authors.lastname: Park
authors.affiliation: Department of Statistics, North Carolina State University, Raleigh, NC 27695-8203, USA.
authors.forename: Ana-Maria
authors.initials: AM
authors.lastname: Staicu
keywords: Dependent functional data; Diffusion Tensor Imaging; Functional principal component analysis; Longitudinal design; Multiple Sclerosis