Please use this identifier to cite or link to this item:
http://hdl.handle.net/2282/397
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| Title: | Noise handling capabilities of multivariate methods |
| Authors: | Ergon, Rolf |
| Issue Date: | 2002 |
| Abstract: | The noise handling capabilities of principal component regression (PCR) and
partial least squares regression (PLSR) are somewhat disputed issues, especially
regarding regressor noise. In an attempt to indicate an answer to the question,
this article presents results from Monte Carlo simulations assuming a multivariate
mixing problem with spectroscopic data. Comparisons with the best linear
unbiased estimator (BLUE) based on Kalman filtering theory are included. The
simulations indicate that both PCR and PLSR perform comparatively well even
at a considerable regressor noise level. The results are also discussed in relation
to estimation of pure spectra for the mixing constituents, i.e. to identification of
the data generating system. In this respect solutions to well-posed least squares
problems serve as references. |
| Keywords: | PCR
PLSR
Prediction
Spectra |
| Publisher: | Norsk forening for automatisering |
| Document type: | Journal article |
| URI: | http://hdl.handle.net/2282/397 |
| Appears in Collections: | Institutt for elektro, IT og kybernetikk
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