<div class="csl-bib-body">
<div class="csl-entry">Filzmoser, P., Serneels, S., Maronna, R., & Croux, C. (2020). Robust Multivariate Methods in Chemometrics. In S. Brown, R. Tauler, & B. Walczak (Eds.), <i>Comprehensive Chemometrics: Chemical and Biochemical Data Analysis</i> (pp. 393–430). Elsevier. https://doi.org/10.1016/B978-0-12-409547-2.14642-6</div>
</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/30120
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dc.description
Public link: https://www.elsevier.com/books/comprehensive-chemometrics/brown/978-0-444-64165-6
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dc.description.abstract
This article presents an introduction to robust statistics with applications of a chemometric nature. Following a description of the basic ideas and concepts behind robust statistics, including how robust estimators can be conceived, the article builds up to the construction (and use) of robust alternatives for some methods for multivariate analysis frequently used in chemometrics, such as principal component analysis and partial least squares. The article then provides an insight into how these robust methods can be used or extended to classification. To conclude, the issue of validation of the results is being addressed: it is shown how uncertainty statements associated with robust estimates, can be obtained.
en
dc.publisher
Elsevier
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dc.title
Robust Multivariate Methods in Chemometrics
en
dc.type
Buchbeitrag
de
dc.type
Book Contribution
en
dc.relation.publication
Comprehensive Chemometrics: Chemical and Biochemical Data Analysis
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dc.relation.isbn
978-0-444-64166-3
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dc.description.startpage
393
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dc.description.endpage
430
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dc.type.category
Edited Volume Contribution
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dc.publisher.place
(*)
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tuw.booktitle
Comprehensive Chemometrics: Chemical and Biochemical Data Analysis
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tuw.peerreviewed
true
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tuw.relation.publisher
Elsevier
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tuw.book.chapter
3.19
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tuw.researchTopic.id
X1
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tuw.researchTopic.name
außerhalb der gesamtuniversitären Forschungsschwerpunkte