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Significance regression: A statistical approach to partial least squares
California Institute of Technology.
KTH, Superseded Departments, Signals, Sensors and Systems. (Signalbehandling, Signal Processing)ORCID iD: 0000-0002-9368-3079
(Swiss Federal Institute of Technology)
(California Institute of Technology)
1997 (English)In: Journal of Chemometrics, ISSN 08869383 (ISSN), Vol. 11, no 4, 283-309 p.Article in journal (Refereed) Published
Abstract [en]

This paper presents a formal framework for deriving partial least squares algorithms from statistical hypothesis testing. This new formulation, significance regression (SR), leads to partial least squares for scalar output problems (PLS1), to a close approximation of a common multivariable partial least squares algorithm (PLS2) under certain model assumptions and to more general methods under less restrictive model assumptions. For models with multiple outputs, SR will be shown to have certain advantages over PLS2. Using the new formulation, a significance test is advanced for determining the number of directions to be used. The prediction and estimation properties of SR are discussed. A brief numerical example illustrates the relationship between SR and PLS2. © 1997 by John Wiley & Sons, Ltd.

Place, publisher, year, edition, pages
1997. Vol. 11, no 4, 283-309 p.
Keyword [en]
Biased regression, Collinearity, Multivariable regression, PLS, Significance testing
National Category
Signal Processing
Research subject
URN: urn:nbn:se:kth:diva-60584DOI: 10.1002/(SICI)1099-128X(199707)11:4<283::AID-CEM475>3.0.CO;2-3OAI: diva2:479512
NR 20140805Available from: 2012-01-17 Created: 2012-01-13 Last updated: 2012-01-17Bibliographically approved

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Hjalmarsson, Håkan
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