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An introduction with medical applications to functional data analysis
Author(s) -
Sørensen Helle,
Goldsmith Jeff,
Sangalli Laura M.
Publication year - 2013
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.5989
Subject(s) - functional data analysis , smoothing , principal component analysis , functional principal component analysis , biomedicine , computer science , data mining , data analysis , artificial intelligence , machine learning , bioinformatics , computer vision , biology
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. Copyright © 2013 John Wiley & Sons, Ltd.