An introduction with medical applications to functional data analysis

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An introduction with medical applications to functional data analysis. / Sørensen, Helle; Goldsmith, Jeff; Sangalli, Laura M.

I: Statistics in Medicine, Bind 32, Nr. 30, 2013, s. 5222-5240.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Sørensen, H, Goldsmith, J & Sangalli, LM 2013, 'An introduction with medical applications to functional data analysis', Statistics in Medicine, bind 32, nr. 30, s. 5222-5240. https://doi.org/10.1002/sim.5989

APA

Sørensen, H., Goldsmith, J., & Sangalli, L. M. (2013). An introduction with medical applications to functional data analysis. Statistics in Medicine, 32(30), 5222-5240. https://doi.org/10.1002/sim.5989

Vancouver

Sørensen H, Goldsmith J, Sangalli LM. An introduction with medical applications to functional data analysis. Statistics in Medicine. 2013;32(30):5222-5240. https://doi.org/10.1002/sim.5989

Author

Sørensen, Helle ; Goldsmith, Jeff ; Sangalli, Laura M. / An introduction with medical applications to functional data analysis. I: Statistics in Medicine. 2013 ; Bind 32, Nr. 30. s. 5222-5240.

Bibtex

@article{e7973cbf66524cde92c98ee4c756cf3f,
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.",
author = "Helle S{\o}rensen and Jeff Goldsmith and Sangalli, {Laura M}",
year = "2013",
doi = "10.1002/sim.5989",
language = "English",
volume = "32",
pages = "5222--5240",
journal = "Statistics in Medicine",
issn = "0277-6715",
publisher = "JohnWiley & Sons Ltd",
number = "30",

}

RIS

TY - JOUR

T1 - An introduction with medical applications to functional data analysis

AU - Sørensen, Helle

AU - Goldsmith, Jeff

AU - Sangalli, Laura M

PY - 2013

Y1 - 2013

N2 - 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.

AB - 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.

U2 - 10.1002/sim.5989

DO - 10.1002/sim.5989

M3 - Journal article

C2 - 24114808

VL - 32

SP - 5222

EP - 5240

JO - Statistics in Medicine

JF - Statistics in Medicine

SN - 0277-6715

IS - 30

ER -

ID: 87416948