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Partial least squares algorithms and methods

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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
Figure 1.

PLS path model representation. The inner model is painted in blue gray, the outer model in sky blue.

[ Normal View | Magnified View ]
Figure 2.

An example of hierarchical path model with three reflective blocks.

[ Normal View | Magnified View ]
Figure 3.

An example of confirmatory path model with four reflective blocks.

[ Normal View | Magnified View ]
Figure 4.

HBAT path model.

[ Normal View | Magnified View ]
Figure 5.

HBAT inner model.

[ Normal View | Magnified View ]

Browse by Topic

Statistical and Graphical Methods of Data Analysis > Multivariate Analysis
Statistical Models > Linear Models
Algorithms and Computational Methods > Least Squares
Statistical Learning and Exploratory Methods of the Data Sciences > Exploratory Data Analysis

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