Journal ArticleDOI
PLS regression methods
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In this paper, the mathematical and statistical structure of PLS regression is developed and the PLS decomposition of the data matrices involved in model building is analyzed. But the PLP regression algorithm can be interpreted in a model building setting.Abstract:
In this paper we develop the mathematical and statistical structure of PLS regression We show the PLS regression algorithm and how it can be interpreted in model building The basic mathematical principles that lie behind two block PLS are depicted We also show the statistical aspects of the PLS method when it is used for model building Finally we show the structure of the PLS decompositions of the data matrices involvedread more
Citations
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PLS-regression: a basic tool of chemometrics
TL;DR: PLS-regression (PLSR) as mentioned in this paper is the PLS approach in its simplest, and in chemistry and technology, most used form (two-block predictive PLS) is a method for relating two data matrices, X and Y, by a linear multivariate model.
Book
Kernel Methods for Pattern Analysis
TL;DR: This book provides an easy introduction for students and researchers to the growing field of kernel-based pattern analysis, demonstrating with examples how to handcraft an algorithm or a kernel for a new specific application, and covering all the necessary conceptual and mathematical tools to do so.
Journal ArticleDOI
A National Customer Satisfaction Barometer: The Swedish Experience:
TL;DR: Swedish companies and some industries monitor customer satisfaction on a continual basis, but Sweden is the first country to do so on a national level as mentioned in this paper. And the annual Customer Satisfaction Baro...
Book
Partial Least Squares for Discrimination
TL;DR: Partial least squares (PLS) was not originally designed as a tool for statistical discrimination as discussed by the authors, but applied scientists routinely use PLS for classification and there is substantial empirical evidence to suggest that it performs well in that role.
Journal ArticleDOI
SIMPLS: an alternative approach to partial least squares regression
TL;DR: De Jong et al. as discussed by the authors proposed SIMPLS, an alternative approach to PLS regression, which calculates the PLS factors directly as linear combinations of the original variables, while obeying certain orthogonality and normalization restrictions.
References
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Journal ArticleDOI
Theory of Reproducing Kernels.
TL;DR: In this paper, a short historical introduction is given to indicate the different manners in which these kernels have been used by various investigators and discuss the more important trends of the application of these kernels without attempting, however, a complete bibliography of the subject matter.
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Cross-Validatory Estimation of the Number of Components in Factor and Principal Components Models
TL;DR: In this article, the rank estimation of the rank A of the matrix Y, i.e., the estimation of how much of the data y ik is signal and how much is noise, is considered.
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An example of 2-block predictive partial least-squares regression with simulated data
Paul Geladi,Bruce R. Kowalski +1 more
TL;DR: In this paper, practical examples with simulated data are described to illustrate the material described in the preceding tutorial on partial least-squares regression, starting from the perfect model for two dimensions, noise, nonlinearities and interference are added gradually in order to study their influence.
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Separation theorems for singular values of matrices and their applications in multivariate analysis
TL;DR: In this paper, separation theorems for singular values of a matrix, similar to the Poincare separation theorem for the eigenvalues of a Hermitian matrix, are proved.
Journal ArticleDOI
Inference in canonical correlation analysis
TL;DR: In this article, the authors examined the Bartlett-Lawley test that the residual population canonical correlation coefficients are zero and obtained a marginal likelihood function for the population coefficients and the maximum marginal likelihood estimates were shown to provide a bias correction.