Robustness for compositional data

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F13%3A33144753" target="_blank" >RIV/61989592:15310/13:33144753 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Robustness for compositional data
Original language description
Compositional data, data containing relative rather than absolute information, need to be transformed to the usual Euclidean geometry before the standard statistical tools can be applied. Different possible transformations and their properties are discussed. For robust multivariate methods based on a robust covariance estimation, it is crucial to use a transformation that avoids singularity issues. Moreover, the robust location and covariance estimators need to be affine equivariant in order to obtain invariance of the results from the transformation used. Here, different robust multivariate methods are discussed for compositional data analysis, like principal component and discriminant analysis, and applied to a data set from geochemistry.
Czech name
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Czech description
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Project
<a href="/en/project/EE2.3.20.0170" target="_blank" >EE2.3.20.0170: Building of Research Team in the Field of Environmental Modeling and the Use of Geoinformation Systems with the Consequence in Participation in International Networks and Programs</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2013
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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