|
|||||
|
|
Covariance-Based Variable Selection for Compositional Data |
|
| Authors: | Karel Hron Peter Filzmoser Sandra Donevska Eva Fi?erová |
| Institution: | 1. Department of Mathematical Analysis and Applications of Mathematics, Faculty of Science, Palacky University, 17. listopadu 12, 771 46, Olomouc, Czech Republic 2. Department of Geoinformatics, Faculty of Science, Palacky University, t?. Svobody 26, 771 46, Olomouc, Czech Republic 3. Department of Statistics and Probability Theory, Vienna University of Technology, Wiedner Hauptstrasse 8-10, 1040, Vienna, Austria |
| Abstract: | 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. |
| Keywords: | |
| 本文献已被 SpringerLink 等数据库收录！ | |