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Clustered Regression Analysis
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
2002 (English)Report (Other academic)
Abstract [en]

Cluster structure in (multicollinear) data can be utilized by pattern recognition methods in order to find adequate subspaces for nonlinear regression. When regressing a particular severely nonlinear function, it is demonstrated that this approach is superior to polynomial PLS. It is also demonstrated that for nonlinear functions, the choice of regressing explained variables onto the explaining variables, or vice-versa, is not arbitrary. Numerical experiments indicate that the classical statistical model is more powerful than the inverse regression approach.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2002. , 8 p.
Series
LiTH-ISY-R, ISSN 1400-3902 ; 2456
Keyword [en]
Nonlinear regression, Subspace regression, Inverse regression
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-55888ISRN: LiTH-ISY-R, 2456OAI: oai:DiVA.org:liu-55888DiVA: diva2:316644
Available from: 2010-04-30 Created: 2010-04-30 Last updated: 2014-09-04Bibliographically approved

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Lindgren, DavidLjung, Lennart

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CiteExportLink to record
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Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
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Output format
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