Results 251 to 260 of about 3,502,968 (324)

Partial least squares regression and projection on latent structure regression (PLS Regression)

WIREs Computational Statistics, 2010
AbstractPartial least squares (PLS) regression (a.k.a. projection on latent structures) is a recent technique that combines features from and generalizes principal component analysis (PCA) and multiple linear regression. Its goal is to predict a set of dependent variables from a set of independent variables or predictors. This prediction is achieved by
H. Abdi
semanticscholar   +4 more sources

A novel robust NL-PLS regression methodology

Chemometrics and Intelligent Laboratory Systems, 2019
Abstract Partial Least Squares-1 (PLS) is known to be a reliable regression methodology. However, the linear form of the resulting predictive model from a PLS regression may yield poor predictive performances in the presence of important nonlinear X-y relations.
Francis B. Lavoie, Koji Muteki, Ryan Gosselin   +2 more
semanticscholar   +4 more sources

PLS generalised linear regression

Computational Statistics & Data Analysis, 2005
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Bastien, Philippe, Espositi Vinzi, Vincenzo, Tenenhaus, M.   +2 more
openaire   +4 more sources

The Sequential and Orthogonalized PLS Regression for Multiblock Regression

Data Handling in Science and Technology, 2019
In this chapter, the sequentially orthogonalized-PLS (SO-PLS) method and some of its main extensions are described and illustrated. Both theoretical aspects and applications on real data are discussed.
Alessandra Biancolillo, Tormod Næs
openaire   +2 more sources

Signal Smoothing with PLS Regression

Analytical Chemistry, 2018
Smoothing of instrumental signals is an important prerequisite in data processing. Various smoothing methods were suggested through the last decades each having their own benefits and drawbacks. Most of the filtering methods are based on averaging in a certain window (e.g., Savitzky-Golay) or on frequency-domain representation (e.g., Fourier filtering).
Vitaly, Panchuk, Valentin, Semenov, Andrey, Legin, Dmitry, Kirsanov   +3 more
openaire   +3 more sources

Quadratic PLS regression

Journal of Chemometrics, 1992
AbstractWe treat here an extension of linear PLS regression to include regression on quadratic PLS components. The quadratic regression can be viewed as a natural extention of linear PLS regression to quadratic PLS according to the H‐principle of mathematical modelling. The numerical implementation is treated in detail.
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PLS-regression: a basic tool of chemometrics

Chemometrics and Intelligent Laboratory Systems, 2001
S. Wold, M. Sjöström, L. Eriksson
semanticscholar   +3 more sources

Variable and subset selection in PLS regression

Chemometrics and Intelligent Laboratory Systems, 2001
The purpose of this paper is to present some useful methods for introductory analysis of variables and subsets in relation to PLS regression. We present here methods that are efficient in finding the appropriate variables or subset to use in the PLS regression.
openaire   +3 more sources

Identification strategy of Fructus Gardeniae and its adulterant based on UHPLC/Q-orbitrap-MS and UHPLC-QTRAP-MS/MS combined with PLS regression model.

Talanta: The International Journal of Pure and Applied Analytical Chemistry, 2023
Fructus Gardeniae (FG) is the desiccative and ripe fruits of Gardenia jasminoides Ellis in the Rubiaceae family, which is a commonly used in traditional Chinese medicine (TCM) for clearing away heat, detoxification, relieving restlessness, and ...
Xue Zhang, Lei Wang, Rongrong Li, Liming Wang, Zhifei Fu, Feng He, Erwei Liu, Lifeng Han   +7 more
semanticscholar   +1 more source

Ridge Regression and PLS Regression

American Review of Mathematics and Statistics, 2023
A brief review of Ridge Regression (RR) and PLS Regression (PLS) is presented. Process and Spectral data are used in the analysis. Both are low-rank data, which is common in chemometric work.
A. Höskuldsson
semanticscholar   +1 more source