## Abstract Ecological studies frequently involve large numbers of variables and observations, and these are often subject to various errors. If some data are not representative of the study population, they tend to bias the interpretation and conclusion of an ecological study. Because of the multi
Principal component analysis for compositional data with outliers
β Scribed by Peter Filzmoser; Karel Hron; Clemens Reimann
- Publisher
- John Wiley and Sons
- Year
- 2009
- Tongue
- English
- Weight
- 483 KB
- Volume
- 20
- Category
- Article
- ISSN
- 1180-4009
- DOI
- 10.1002/env.966
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Two of the most popular descriptive multivariate methods currently employed are the principal component analysis and canonical variate analysis methods. Canonical variate analysis is the most appropriate technique to use whenever the multivariate data are grouped and to discriminate group dierences
## Abstract We describe a principal component analysis (PCA) method for functional magnetic resonance imaging (fMRI) data based on functional data analysis, an advanced nonparametric approach. The data delivered by the fMRI scans are viewed as continuous functions of time sampled at the interscan i