Results 281 to 290 of about 3,502,968 (324)

A simple iteration algorithm for PLS regression

Journal of Chemometrics, 1995
AbstractA simple iteration algorithm that is faster and less memory‐intensive than the NIPALS iteration algorithm for PLS regression is presented. The iteration algorithm is obtained by treating the orthogonal expansion or decomposition of a matrix X as an extremum problem subject to normalization and orthogonality constraint conditions and then ...
Eryi Zhu, Ramon M. Barnes
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PLS regression on wavelet compressed NIR spectra

Chemometrics and Intelligent Laboratory Systems, 1998
Abstract Today, good compression methods are more and more needed, due to the ever increasing amount of data that is being collected. The mere thought of the computational power demanded to calculate a regression model on a large data set with many thousands of variables can often be depressing.
Johan Trygg, Svante Wold
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Experimental design and priority PLS regression

Journal of Chemometrics, 1996
In this paper some fundamental issues of experimental design are treated. The rules, ideas and algorithms of the H-principle are used to analyse models that are derived from experimental design. A new approach to analysing models from experimental design is proposed that is called priority PLS regression.
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PLS Regression and PLS Path Modeling for Multiple Table Analysis

2004
A situation where J blocks of variables are observed on the same set of individuals is considered in this paper. A factor analysis logic is applied to tables instead of individuals. The latent variables of each block should well explain their own block and in the same time the latent variables of same rank should be as positively correlated as possible.
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PLS Typological Regression: Algorithmic, Classification and Validation Issues

2005
Classification, within a PLS regression framework, is classically meant in the sense of the SIMCA methodology, i.e. as the assignment of statistical units to a-priori defined classes. As a matter of fact, PLS components are built with the double objective of describing the set of explanatory variables while predicting the set of response variables ...
V. Esposito Vinzi, LAURO, NATALE, S. Amato   +2 more
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Application of PLS and Sparse PLS regression in genomic selection

2010
absent
Colombani, Carine, Legarra, Andres, Croiseau, Pascal, Guillaume, F., Fritz, S., Ducrocq, Vincent, Robert-Granié, Christèle   +6 more
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Multi-group PLS Regression: Application to Epidemiology

2013
For the investigation of the relationships between two datasets where the individuals are divided into groups a simple procedure called multi-group pls regression is discussed. It is a straightforward extension of pls regression to take account of the group structure.
Aida Eslami, El Mostafa Qannari, Achim Kohler, Stéphanie Bougeard   +3 more
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Stepwise PLS regression and multiple regression, Application to the selection of variables, the choice of the number of PLS components and PLS generalized linear regression

2002
" Stepwise PLS regression and multiple regression, Application to the selection of variables, the choice of the number of PLS components and PLS generalized linear regression "CIRO'02. Marrakech, Juin 2002.
Gonzalez, Pierre-Louis, Tenenhaus, M.
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PLS regression for multivariate functional data

2014
In this paper we consider the linear regression model with multivariate functional random variable predictorand vectorial response. We present the PLS estimation when the predictor is approximated in a finite dimensional space of functions.
Preda, Cristian, Saporta, Gilbert
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EVALUATING RIDGE REGRESSION VERSUS PLS REGRESSION: A COMPREHENSIVE STUDY

In the realm of statistical modeling, a longstanding debate has persisted regarding Ridge Regression (RR) and Partial Least Squares (PLS) as preferred methodologies. Statisticians argue in favor of RR, rooted in a well-established mathematical foundation, while PLS finds favor among chemometricians due to its reliance on orthogonal variable projection.
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