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[ER12] Partial least squares algorithms and methods
Revue Internationale avec comité de lecture : Journal WIREs Comput Stat, vol. 5, pp. 1-19, 2012, (doi:10.1002/wics.1239)Mots clés: NIPALS; shrinkage; multi-block data; path models
Résumé:
Partial least squares (PLS) refers to a set of iterative algorithms based on least squares that implement a broad spectrum of both explanatory and exploratory multivariate techniques, from regression to path modeling, and from principal
component to multi-block data analysis. This article focuses on PLS regression and PLS path modeling, which are PLS approaches to regularized regression and to predictive path modeling. The computational flows and the optimization criteria of these methods are reviewed in detail, as well as the tools for the assessment and interpretation of PLS models. The most recent developments and some of the most promising on going researches are enhanced.
Equipe:
vertigo
BibTeX
| @article { | |||
| ER12, | |||
| title | = | "{Partial least squares algorithms and methods}", | |
| author | = | "V. Esposito Vinzi and G. Russolillo", | |
| journal | = | "WIREs Comput Stat", | |
| year | = | 2012, | |
| volume | = | 5, | |
| pages | = | "1-19", | |
| doi | = | "10.1002/wics.1239", | |
| } | |||
