An introduction with medical applications to functional data analysis
Keywords
Statistical learning
Code:
23/2013
Title:
An introduction with medical applications to functional data analysis
Date:
Wednesday 22nd May 2013
Author(s):
Sørensen, H.; Goldsmith, J.; Sangalli, L.M.
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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 the techniques are applied 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.
This report, or a modified version of it, has been also submitted to, or published on
Helle Sørensen, Jeff Goldsmith, Laura M. Sangalli (2013), An introduction with medical applications to functional data analysis, Statistics in Medicine, 32, 5222-5240.
Helle Sørensen, Jeff Goldsmith, Laura M. Sangalli (2013), An introduction with medical applications to functional data analysis, Statistics in Medicine, 32, 5222-5240.