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Journal ArticleDOI

PLS-regression: a basic tool of chemometrics

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TLDR
PLS-regression (PLSR) as mentioned in this paper is the PLS approach in its simplest, and in chemistry and technology, most used form (two-block predictive PLS) is a method for relating two data matrices, X and Y, by a linear multivariate model.
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This article is published in Chemometrics and Intelligent Laboratory Systems.The article was published on 2001-10-28. It has received 7861 citations till now. The article focuses on the topics: Partial least squares regression.

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Potential of field hyperspectral imaging as a non destructive method to assess leaf nitrogen content in Wheat

TL;DR: In this article, the authors proposed a non-destructive method based on leaf optical properties for a nondestructive diagnosis to replace Nitrogen Nutrition Index which is a costly and destructive method.
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The EnMAP-Box—A Toolbox and Application Programming Interface for EnMAP Data Processing

TL;DR: An overview of the EnMAP-Box is given, typical workflows along an application example are explained, and the concept for making it a frequently used and constantly extended platform for imaging spectroscopy applications is exemplified.
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PCA and PLS with very large data sets

TL;DR: A multivariate approach based on projections—PCA and PLS—was introduced to cope with the rapidly increasing volumes of data produced in chemical laboratories and showed promising results.
References
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Book

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.
Book

A User's Guide to Principal Components

TL;DR: In this paper, the authors present a directory of Symbols and Definitions for PCA, as well as some classic examples of PCA applications, such as: linear models, regression PCA of predictor variables, and analysis of variance PCA for Response Variables.
Journal ArticleDOI

A Leisurely Look at the Bootstrap, the Jackknife, and Cross-Validation

TL;DR: This paper reviewed the nonparametric estimation of statistical error, mainly the bias and standard error of an estimator, or the error rate of a prediction rule, at a relaxed mathematical level, omitting most proofs, regularity conditions and technical details.
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