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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)In: Proceedings of the 41st IEEE Conference on Decision and Control, 2002, 1838-1844 vol.2 p.Conference paper, Published paper (Refereed)
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
2002. 1838-1844 vol.2 p.
Keyword [en]
Nonlinear regression, Subspace regression, Inverse regression
National Category
Engineering and Technology Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-90316DOI: 10.1109/CDC.2002.1184791ISBN: 0-7803-7516-5 (print)OAI: oai:DiVA.org:liu-90316DiVA: diva2:613616
Conference
41st IEEE Conference on Decision and Control, Las Vegas, NV, USA, December, 2002
Available from: 2013-03-29 Created: 2013-03-24 Last updated: 2013-03-29

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Lindgren, DavidLjung, Lennart

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