Principal component analysis for compositional data with outliers

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F09%3A00010064" target="_blank" >RIV/61989592:15310/09:00010064 - isvavai.cz</a>
Result on the web
—
DOI - Digital Object Identifier
—

Result language
angličtina
Original language name
Principal component analysis for compositional data with outliers
Original language description
Compositional data (almost all data in geochemistry) are closed data, that is they usually sum up to a constant (e.g. weight percent, wt.%) and carry only relative information. Thus, the covariance structure of compositional data is strongly biased and results of many multivariate techniques become doubtful without a proper transformation of the data. The centred logratio transformation (clr) is often used to open closed data. However the transformed data do not have full rank following a logratio transformation and cannot be used for robust multivariate techniques like principal component analysis (PCA). Here we propose to use the isometric logratio transformation (ilr) instead. However, the ilr transformation has the disadvantage that the resulting new variables are no longer directly interpretable in terms of the originally entered variables. Here we propose a technique how the resulting scores and loadings of a robust PCA on ilr transformed data can be back-transformed and interpre
Czech name
—
Czech description
—

Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
BA - General mathematics
OECD FORD branch
—

Publication year
2009
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Similar results(10)