Interpretation of multivariate outliers for compositional data

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F12%3A33140615" target="_blank" >RIV/61989592:15310/12:33140615 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1016/j.cageo.2011.06.014" target="_blank" >http://dx.doi.org/10.1016/j.cageo.2011.06.014</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.cageo.2011.06.014" target="_blank" >10.1016/j.cageo.2011.06.014</a>

Result language
angličtina
Original language name
Interpretation of multivariate outliers for compositional data
Original language description
Compositional data--and most data in geochemistry are of this type--carry relative rather than absolute information. For multivariate outlier detection methods this implies that not the given data but appropriately transformed data need to be used. We use the isometric logratio (ilr) transformation that seems to be generally the most proper one for theoretical and practical reasons. In this space it is difficult to interpret the outliers, because the reason for outlyingness can be complex. Therefore weintroduce tools that support the interpretation of the outliers by representing the multivariate information in biplots, maps, and univariate scatter plots.
Czech name
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Czech description
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Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
BB - Applied statistics, operational research
OECD FORD branch
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Publication year
2012
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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