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      Tritium as a tool to assess leachate contamination: An example from Conversano landfill (Southern Italy)

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      Journal of Geochemical Exploration
      Elsevier BV

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          Principal component analysis: a review and recent developments.

          Large datasets are increasingly common and are often difficult to interpret. Principal component analysis (PCA) is a technique for reducing the dimensionality of such datasets, increasing interpretability but at the same time minimizing information loss. It does so by creating new uncorrelated variables that successively maximize variance. Finding such new variables, the principal components, reduces to solving an eigenvalue/eigenvector problem, and the new variables are defined by the dataset at hand, not a priori, hence making PCA an adaptive data analysis technique. It is adaptive in another sense too, since variants of the technique have been developed that are tailored to various different data types and structures. This article will begin by introducing the basic ideas of PCA, discussing what it can and cannot do. It will then describe some variants of PCA and their application.
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            An analysis of variance test for normality (complete samples)

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              The varimax criterion for analytic rotation in factor analysis

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                Author and article information

                Journal
                Journal of Geochemical Exploration
                Journal of Geochemical Exploration
                Elsevier BV
                03756742
                April 2022
                April 2022
                : 235
                : 106939
                Article
                10.1016/j.gexplo.2021.106939
                7bb76077-9f49-4be4-add4-e8c62a1f06a8
                © 2022

                https://www.elsevier.com/tdm/userlicense/1.0/

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