<div class="csl-bib-body">
<div class="csl-entry">Avalos Pacheco, A., & Roberta De Vito. (2023). Integrative Factor Models for Biomedical Applications. In <i>CLADAG 2023 Book of Abstract and Short Papers : 14th Scientific Meeting of the Classification and Data Analysis Group</i> (pp. 50–53).</div>
</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/192414
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dc.description.abstract
Data-integration of multiple studies is key to understanding and gaining knowledge in statistical research. However, such data present artifactual sources of variation, also known as covariate effects. Covariate effects can be complex and can lead to systematic biases. If not corrected, these biases may lead to unreliable inferences. Here, we will present novel sparse latent factor regression and multi-study factor regression models to integrate heterogeneous data.
en
dc.language.iso
en
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dc.subject
factor regression
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dc.subject
multi-study factor analysis
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dc.subject
sparsity
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dc.subject
non-local priors
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dc.subject
scalable algorithms
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dc.title
Integrative Factor Models for Biomedical Applications
en
dc.type
Inproceedings
en
dc.type
Konferenzbeitrag
de
dc.relation.isbn
978-88-9193-563-2
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dc.description.startpage
50
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dc.description.endpage
53
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dc.type.category
Full-Paper Contribution
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tuw.booktitle
CLADAG 2023 Book of Abstract and Short Papers : 14th Scientific Meeting of the Classification and Data Analysis Group
