<div class="csl-bib-body">
<div class="csl-entry">Puchhammer, P., Filzmoser, P., & Wilms, I. (2024, April 4). <i>Groupwise sparse PCA for spatial data</i> [Conference Presentation]. Österreichische Statistiktage 2024 (2024, Wien), Wien, Austria. http://hdl.handle.net/20.500.12708/200066</div>
</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/200066
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dc.description.abstract
Many approaches for principal component analysis (PCA) with sparse entries are available. While sparsity facilitates interpretation, it also facilitates visualization of the complex result of multiple PCAs. Still, none of these approaches is applicable for PCA of a set of covariance matrices simultaneously. This becomes essential for spatial data, where covariance matrices for different locations can be calculated. Thus, we develop an algorithm to solve the complex optimization problem and apply it to simulations and real data examples.
en
dc.description.sponsorship
European Commission
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dc.language.iso
en
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dc.subject
Robust statistics
en
dc.subject
Spatial data
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dc.title
Groupwise sparse PCA for spatial data
en
dc.type
Presentation
en
dc.type
Vortrag
de
dc.contributor.affiliation
Maastricht University, Netherlands (the)
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dc.relation.grantno
SEMACRET - 101057741 - GAP-101057741
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dc.type.category
Conference Presentation
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tuw.project.title
Sustainable exploration for orthomagmatic (critical) raw materials in the EU: Charting the road to the green energy transition.
