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[Rus12] Non-Metric Partial Least Squares
Revue Internationale avec comité de lecture : Journal Electronic Journal of Statistics, vol. 6, pp. 1641-1669, 2012, (doi:10.1214/12-EJS724)Mots clés: Optimal Scaling; NIPALS; PLS Regression; PLS Path Modeling; non-linearity
Résumé:
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
Commentaires:
Available at http://projecteuclid.org/euclid.ejs/1348665231
Equipe:
vertigo
BibTeX
| @article { | |||
| Rus12, | |||
| title | = | "{Non-Metric Partial Least Squares}", | |
| author | = | "G. Russolillo", | |
| journal | = | "Electronic Journal of Statistics", | |
| year | = | 2012, | |
| volume | = | 6, | |
| pages | = | "1641-1669", | |
| note | = | "{Available at http://projecteuclid.org/euclid.ejs/1348665231}", | |
| doi | = | "10.1214/12-EJS724", | |
| } | |||
