title: An introduction with medical applications to functional data analysis.
abstract: Functional data are data that can be represented by suitable functions, such as curves (potentially multi-dimensional) or surfaces. This paper gives an introduction to some basic but important techniques for the analysis of such data, and we apply the techniques to two datasets from biomedicine. One dataset is about white matter structures in the brain in multiple sclerosis patients; the other dataset is about three-dimensional vascular geometries collected for the study of cerebral aneurysms. The techniques described are smoothing, alignment, principal component analysis, and regression.
authors.affiliation: Laboratory for Applied Statistics, Department of Mathematical Sciences, University of Copenhagen, Denmark.
authors.forename: Helle
authors.initials: H
authors.lastname: Sørensen
authors.forename: Jeff
authors.initials: J
authors.lastname: Goldsmith
authors.forename: Laura M
authors.initials: LM
authors.lastname: Sangalli
mesh_terms: D003627:Data Interpretation, Statistical; D006801:Humans; D002532:Intracranial Aneurysm / Q000473:pathology; D018810:Magnetic Resonance Angiography; D008279:Magnetic Resonance Imaging; D009103:Multiple Sclerosis / Q000473:pathology; D025341:Principal Component Analysis / Q000379:methods*; D012044:Regression Analysis
keywords: curve alignment; functional principal component analysis; functional regression; smoothing