Covariance-Based Variable Selection for Compositional Data

Autor:	Karel Hron, Peter Filzmoser, Eva Fišerová, Sandra Donevska
Rok vydání:	2013
Předmět:	Reduction (complexity) Mathematics (miscellaneous) Biplot Statistics Principal component analysis General Earth and Planetary Sciences Feature selection Covariance Multivariate statistical Compositional data Interpretation (model theory) Mathematics
Zdroj:	Mathematical Geosciences. 45:487-498
ISSN:	1874-8953 1874-8961
DOI:	10.1007/s11004-013-9450-9
Popis:	Omitting variables in compositional data analysis may lead to a substantial change in results from that of multivariate statistical analysis. In particular, this is the case for principal component analysis and the compositional biplot, where both the interpretation of loadings and scores of the remaining subcomposition are affected. A stepwise procedure is introduced that allows for a reduction of the original composition to a subcomposition by avoiding a substantial change of the information, like those carried by the compositional biplot. The subcomposition is easier to handle and interpret. Numerical results give evidence of the usefulness of the procedure.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::9c090bba873978350496c9acf0b2caaa https://doi.org/10.1007/s11004-013-9450-9 Zobrazit plný text záznamu Full text from SpringerLink