Number of latents The same number of factors will be extracted for PLS responses as for PLS factors. The researcher must specify how many latents to extract (in SPSS the default is 5). There is no one criterion for deciding how many latents to employ. Common alternatives are: 1. Cross-validating the model with increasing numbers of factors, then choosing the number with minimum prediction error...

Pls regression is a recent technique that generalizes and combines features from principal component analysis and multiple regression. It is particularly useful when we need to predict a set of dependent variables from a (very) large set of independent variables (i.e., predictors). It originated in the social sciences (specifically economy, Herman Wold 1966) but became popular first in chemomet...

For the purpose of exploring and modelling the relationships between a dataset and several datasets, multiblock Partial Least Squares is a widely-used regression technique. It is designed as an extension of PLS which aims at linking two datasets. In the same vein, we propose an extension of Redundancy Analysis to the multiblock setting. We show that PLS and multiblock Redundancy Analysis aim at...

Partial least squares is a popular method for soft modelling in industrial applications. This paper introduces the basic concepts and illustrates them with a chemometric example. An appendix describes the experimental PLS procedure of SAS/STAT software.

PLS univariate regression is a model linking a dependent variable y to a set X= {x1; : : : ; xp} of (numerical or categorical) explanatory variables. It can be obtained as a series of simple and multiple regressions. By taking advantage from the statistical tests associated with linear regression, it is feasible to select the signi6cant explanatory variables to include in PLS regression and to ...

In this work, we develop a fast sequential lowrank tensor regression framework, namely recursive higher-order partial least squares (RHOPLS). It addresses the great challenges posed by the limited storage space and fast processing time required by dynamic environments when dealing with largescale high-speed general tensor sequences. Smartly integrating a low-rank modification strategy of Tucker...

How to relate two blocks of variables? Partial least squares Analysis two-block pls u, v u'u = v'v = 1 Low-dimensional representation of the pattern of correlations/ covariances between two blocks of variables: A second dimension can be computed as directions orthogonal to the first ones, accounting for the second most correlation/covariance. A direction in each of the two data spaces, for whic...

Six popular approaches of «NIR spectrum–property» calibration model building are compared in this work on the basis of a gasoline spectral data. These approaches are: multiple linear regression (MLR), principal component regression (PCR), linear partial least squares regression (PLS), polynomial partial least squares regression (Poly-PLS), spline partial least squares regression (Spline-PLS) an...

the present study evaluates effects of organizational culture on organizational commitment with emphasis on the role of mediator entrepreneurial orientation departments within the material procurement management at national iranian south oil company. statistic population included of 470 experts and managers, 23 departments within the material procurement management that using stratified random ...

This paper presents regression models obtained from a process of blind prediction of peptide binding affinity from provided descriptors for several distinct datasets as part of the 2006 Comparative Evaluation of Prediction Algorithms (COEPRA) contest. This paper finds that kernel partial least squares, a nonlinear partial least squares (PLS) algorithm, outperforms PLS, and that the incorporatio...