Non-Metric Partial Least Squares

In this paper I review covariance-based Partial Least Squares (PLS) methods, focusing on common features of their respective algorithms and optimization criteria. I then show how these algorithms can be adjusted for use as optimal scaling tools. Three new PLS-type algorithms are proposed for the ana...

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Bibliographic Details
Published inElectronic journal of statistics Vol. 6; no. none
Main Author Russolillo, Giorgio
Format Journal Article
LanguageEnglish
Published Shaker Heights, OH : Institute of Mathematical Statistics 01.01.2012
Subjects
Statistics
PLS Path Modeling
AMS 2000 subject classifications: Primary 62-07 62H25 Optimal Scaling NIPALS PLS Regression PLS Path Modeling non-linearity
AMS 2000 subject classifications: Primary 62-07
NIPALS
PLS Regression
non-linearity
62H25 Optimal Scaling
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Summary:In this paper I review covariance-based Partial Least Squares (PLS) methods, focusing on common features of their respective algorithms and optimization criteria. I then show how these algorithms can be adjusted for use as optimal scaling tools. Three new PLS-type algorithms are proposed for the analysis of one, two or several blocks of variables: the Non-Metric NIPALS, the Non-Metric PLS Regression and the Non-Metric PLS Path Modeling, respectively. These algorithms extend the applicability of PLS methods to data measured on different measurement scales, as well as to variables linked by non-linear relationships.
ISSN:1935-7524
1935-7524
DOI:10.1214/12-EJS724