Chemical data gleaned from instrumental measurements, such as spectroscopy and chromatography, are often contaminated by multiple sources of error that vary during data collection. Abstract factor analysis (AFA) of such data invariably leads to an excessive number of factors. Various sources of expe
✦ LIBER ✦
Note on a modified Faber–Kowalski F-test for abstract factor analysis
✍ Scribed by Nicolaas (Klaas) M. Faber
- Publisher
- John Wiley and Sons
- Year
- 2000
- Tongue
- English
- Weight
- 33 KB
- Volume
- 14
- Category
- Article
- ISSN
- 0886-9383
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Determining the pseudorank of an experimental data matrix, i.e. the mathematical rank in absence of noise, is a fundamental problem in multivariate data analysis. The prime tool for performing this task is abstract factor analysis (AFA). 1 If an estimate of the noise variance is available one may si