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 ...

Partial least squares path modeling (PLS) has seen increased use in the information systems research community. One of the stated key advantages of PLS is that it weights the indicator variables based on the strength of the relationship between the indicators and the underlying constructs, which presumably decreases the effect of measurement error in the analysis results. In this paper we argue...

We propose a novel framework that combines penalization techniques with Partial Least Squares (PLS). We focus on two important applications. (1) We combine PLS with a roughness penalty to estimate high-dimensional regression problems with functional predictors and scalar response. (2) Starting with an additive model, we expand each variable in terms of a generous number of B-Spline basis functi...

This research-in-progress paper proposes the use of a statistical approach named Partial Least squares (PLS) to define the relationships between Critical Success Factors (CSFs) for ERP implementation projects. Some researchers have noted that there are relationships between these CSFs. However, no one has yet tried to define in a formal way these relationships. In this paper we present an overv...

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 variance and with no assurance that individual component structures are directly interpretable or that t...

When dealing with graphical Gaussian models for gene regulatory networks, the major problem is to compute the matrix of partial correlations. Based on the close connection between partial correlations and least squares regression, we suggest estimation of high-dimensional gene networks in terms of partial least squares (PLS) regression and the adaptive Lasso, respectively. In a simulation study...

Random Forests were introduced by Breiman for feature (variable) selection and improved predictions for decision tree models. The resulting model is often superior to Adaboost and bagging approaches. In this paper the random forest approach is extended for variable selection with other learning models, in this case partial least squares (PLS) and kernel partial least squares (K-PLS) to estimate...

In the current paper the decoding algorithms for motor-related BCI systems for continuous upper limb trajectory prediction are considered. Two methods for the smooth prediction, namely Sobolev and Polynomial Penalized Multi-Way Partial Least Squares (PLS) regressions, are proposed. The methods are compared to the Multi-Way Partial Least Squares and Kalman Filter approaches. The comparison demon...

Partial Least Squares (PLS) is a standard statistical method in chemometrics. It can be considered as an incomplete, or “partial”, version of the Least Squares estimator of regression, applicable when high or perfect multicollinearity is present in the predictor variables. The Least Squares estimator is well-known to be an optimal estimator for regression, but only when the error terms are norm...