Smoothed Linear Modeling for Smooth Spectral Data
Joint Authors
Hawkins, Douglas M.
Maboudou-Tchao, Edgard M.
Source
International Journal of Spectroscopy
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-06-06
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
Classification and prediction problems using spectral data lead to high-dimensional data sets.
Spectral data are, however, different from most other high-dimensional data sets in that information usually varies smoothly with wavelength, suggesting that fitted models should also vary smoothly with wavelength.
Functional data analysis, widely used in the analysis of spectral data, meets this objective by changing perspective from the raw spectra to approximations using smooth basis functions.
This paper explores linear regression and linear discriminant analysis fitted directly to the spectral data, imposing penalties on the values and roughness of the fitted coefficients, and shows by example that this can lead to better fits than existing standard methodologies.
American Psychological Association (APA)
Hawkins, Douglas M.& Maboudou-Tchao, Edgard M.. 2013. Smoothed Linear Modeling for Smooth Spectral Data. International Journal of Spectroscopy،Vol. 2013, no. 2013, pp.1-8.
https://search.emarefa.net/detail/BIM-484402
Modern Language Association (MLA)
Hawkins, Douglas M.& Maboudou-Tchao, Edgard M.. Smoothed Linear Modeling for Smooth Spectral Data. International Journal of Spectroscopy No. 2013 (2013), pp.1-8.
https://search.emarefa.net/detail/BIM-484402
American Medical Association (AMA)
Hawkins, Douglas M.& Maboudou-Tchao, Edgard M.. Smoothed Linear Modeling for Smooth Spectral Data. International Journal of Spectroscopy. 2013. Vol. 2013, no. 2013, pp.1-8.
https://search.emarefa.net/detail/BIM-484402
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-484402