Partial least squares (PLS) has been used in multivariate analysis of functional magnetic resonance imaging (fMRI) data as a way of incorporating information about the underlying experimental paradigm. In comparison, principal component analysis (PCA) extracts structure merely by summarizing varianc
A partial-least-squares approach to interpretative analysis of multivariate data
β Scribed by Olav M. Kvalheim
- Publisher
- Elsevier Science
- Year
- 1988
- Tongue
- English
- Weight
- 952 KB
- Volume
- 3
- Category
- Article
- ISSN
- 0169-7439
No coin nor oath required. For personal study only.
π SIMILAR VOLUMES
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 l
Correspondence analysis partial least squares (CA-PLS) has been compared with PLS conceming classification and prediction of unimodal growth temperature data and an example using infrared (IR) spectroscopy for predicting amounts of chemicals in mixtures. CA-PLS was very effective for ordinating the
The graphical Zimm method for in terpretation of light-scattering experimental data is presen ted in a numerical variant where the least squares calculation procedure is used for averaging the data scatter; a computer program in FORTRAN IV language is given.