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Generalized reduced rank latent factor regression for high dimensional tensor fields, and neuroimaging-genetic applications

Abstract:

We propose a generalized reduced rank latent factor regression model (GRRLF) for the analysis of tensor field responses and high dimensional covariates. The model is motivated by the need from imaging-genetic studies to identify genetic variants that are associated with brain imaging phenotypes, often in the form of high dimensional tensor fields. GRRLF identifies from the structure in the data the effective dimensionality of the data, and then jointly performs dimension reduction of the cova...

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Publication status:
Published
Peer review status:
Peer reviewed
Version:
Accepted manuscript

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Publisher copy:
10.1016/j.neuroimage.2016.08.027

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Institution:
University of Oxford
Division:
Medical Sciences Division
Department:
Nuffield Dept of Population Health
Subgroup:
Clinical Trial Service Unit
ORCID:
0000-0002-4516-5103
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More by this author
ORCID:
0000-0003-3025-1292
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Publisher:
Elsevier Publisher's website
Journal:
NeuroImage Journal website
Volume:
144
Issue:
Pt A
Pages:
35-57
Publication date:
2016-09-22
Acceptance date:
2016-08-14
DOI:
EISSN:
1095-9572
ISSN:
1053-8119
Pubs id:
pubs:698329
URN:
uri:48301336-1564-4885-ba6c-5d8d3af51091
UUID:
uuid:48301336-1564-4885-ba6c-5d8d3af51091
Local pid:
pubs:698329

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