Principal component analysis for compositional data with outliers

Kód výsledku v 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>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Principal component analysis for compositional data with outliers

Popis výsledku v původním jazyce

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

Název v anglickém jazyce

Principal component analysis for compositional data with outliers

Popis výsledku anglicky

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

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

BA - Obecná matematika

OECD FORD obor

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Projekt

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Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2009

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Environmetrics

ISSN

1180-4009

e-ISSN

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Svazek periodika

20

Číslo periodika v rámci svazku

6

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

12

Strana od-do

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Kód UT WoS článku

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EID výsledku v databázi Scopus

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