Partial least squares algorithms and methods. (26th December 2012)

• Record Type: Journal Article
• Authors: Esposito Vinzi, Vincenzo; Russolillo, Giorgio
• Is Part Of: Wiley interdisciplinary reviews. Volume 5:Number 1(2013)
• Page Start: 1
• Page End: 19
• Publication Date: 2012-12-26
• Subjects: NIPALS -- shrinkage -- multi‐block data -- path models; Mathematical statistics -- Data processing -- Periodicals; Science -- Data processing -- Periodicals; Social sciences -- Data processing -- Periodicals; Mathematical statistics -- Periodicals; 519.50285
• DOI: 10.1002/wics.1239
• Languages: English
• ISSNs: 1939-5108

Abstract

Abstract: 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. WIREs Comput Stat 2013, 5:1–19. doi: 10.1002/wics.1239 This article is categorized under: Statistical Learning and Exploratory Methods of the Data Sciences > Exploratory Data Analysis Statistical and Graphical Methods of Data Analysis > Multivariate Analysis Statistical Models > Linear Models Algorithms and Computational Methods > Least Squares