Cyclic Subspace Regression
AbstractBy use of cyclic subspaces, explicit connections between principal component regression (PCR) and partial least squares (PLS) are established that shed light onto why one method works better than the other. These connections clearly identify how both methods make use of calibration data in prediction. Moreover, developments leading to these connections show that they are particular manifestations of a more general easily described and implemented regression/prediction process referred to as cyclic subspace regression (CSR). This process not only contains PCR, PLS, and LS (least squares) as special cases but, also a finite number of other related intermediate or transitional regression techniques. Moreover, CSR shows that PCR, PLS, LS, and the related intermediates can be implemented by the same general procedure and that they differ only in the amount of information used from calibration data matrices. In addition to setting out the CSR procedure, the paper also supplies a robust numerical algorithm for its implementation which is used to show how procedures contained within CSR perform on a chemical data set.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Bibliographic InfoArticle provided by Elsevier in its journal Journal of Multivariate Analysis.
Volume (Year): 65 (1998)
Issue (Month): 1 (April)
Contact details of provider:
Web page: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description
You can help add them by filling out this form.
CitEc Project, subscribe to its RSS feed for this item.
- Kondylis, Athanassios & Whittaker, Joe, 2008. "Spectral preconditioning of Krylov spaces: Combining PLS and PC regression," Computational Statistics & Data Analysis, Elsevier, vol. 52(5), pages 2588-2603, January.
- Lang, Patrick & Gironella, Ann & Venema, Rienk, 2007. "Properties of cyclic subspace regression," Journal of Multivariate Analysis, Elsevier, vol. 98(3), pages 625-637, March.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Wendy Shamier).
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.