Journal ArticleDOI
Partial least-squares regression: a tutorial
Paul Geladi,Bruce R. Kowalski +1 more
TLDR
In this paper, a tutorial on the Partial Least Squares (PLS) regression method is provided, and an algorithm for a predictive PLS and some practical hints for its use are given.About:
This article is published in Analytica Chimica Acta.The article was published on 1986-01-01. It has received 6393 citations till now. The article focuses on the topics: Partial least squares regression & Regression analysis.read more
Citations
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Principal component analysis
TL;DR: Principal Component Analysis is a multivariate exploratory analysis method useful to separate systematic variation from noise and to define a space of reduced dimensions that preserve noise.
Book
Applied Predictive Modeling
Max Kuhn,Kjell Johnson +1 more
TL;DR: This research presents a novel and scalable approach called “Smartfitting” that automates the very labor-intensive and therefore time-heavy and therefore expensive and expensive process of designing and implementing statistical models for regression models.
Journal ArticleDOI
Nonlinear principal component analysis using autoassociative neural networks
TL;DR: The NLPCA method is demonstrated using time-dependent, simulated batch reaction data and shows that it successfully reduces dimensionality and produces a feature space map resembling the actual distribution of the underlying system parameters.
Journal ArticleDOI
Partial least-squares methods for spectral analyses. 1. Relation to other quantitative calibration methods and the extraction of qualitative information
TL;DR: Partial least squares (PLS) as discussed by the authors is one of the most popular spectral analysis methods for spectral analysis, which is composed of a series of simpllfled classical least-squares (CLS) and ILS steps.
Book
Partial Least Squares for Discrimination
TL;DR: Partial least squares (PLS) was not originally designed as a tool for statistical discrimination as discussed by the authors, but applied scientists routinely use PLS for classification and there is substantial empirical evidence to suggest that it performs well in that role.
References
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Applied Regression Analysis
Norman R. Draper,Harry Smith +1 more
TL;DR: In this article, the Straight Line Case is used to fit a straight line by least squares, and the Durbin-Watson Test is used for checking the straight line fit.
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Regression Diagnostics: Identifying Influential Data and Sources of Collinearity
TL;DR: In this article, the authors present a method for detecting and assessing Collinearity of observations and outliers in the context of extensions to the Wikipedia corpus, based on the concept of Influential Observations.
Journal ArticleDOI
The Collinearity Problem in Linear Regression. The Partial Least Squares (PLS) Approach to Generalized Inverses
TL;DR: In this article, the use of Partial Least Squares (PLS) for handling collinearities among the independent variables X in multiple regression is discussed, and successive estimates are obtained using the residuals from previous rank as a new dependent variable y.
Journal ArticleDOI
Regression Diagnostics: Identifying Influential Data and Sources of Collinearity
Journal ArticleDOI
Spectrophotometric multicomponent analysis applied to trace metal determinations
TL;DR: In this paper, partial least-squares analysis in latent variables has been used for the analysis of mixture components with low spectral selectivity, namely, in the ultraviolet, visible, and infrared spectral range.