Principal Components Analysis With Nonlinear Optimal Scaling Transformations for Ordinal and Nominal Data
By Jacqueline J. Meulman, A. J. Kooij, Willem J. Heiser. 2005
304 citations recorded by the source corpus.
Subfields: Food Science, Statistics and Probability
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Citations in the corpus
- On the Schoenberg Transformations in Data Analysis: Theory and Illustrations (2010, background): “…or set a i = 0 for objects whose behavior might influence excessively the overall configuration, as in the treatment of “ supplementary elements ” in Correspondence Analysis (see e.g. Benz´ecri 1992; Lebart, Morineau and Piron 1998; Meulman, van der Kooij and Heiser 2004; Greenacre and Blasius 2006).”
- Do cooperative enterprises create social trust? (2012, unknown): “10 We use the Categorical Principal Component Analysis (CatPCA; Meulman et al. 2004) for quantifying ordinal categories, with the number of the components p = 2, the number of the assumed subdimensions for the job motivations.”
- Nonlinear Principal Components Analysis With CATPCA: A Tutorial (2012, background): “Technical details are irrelevant here, and can be found in, for example, Linting, Meulman, Groenen, and Van der Kooij (2007a), and Meulman, Van der Kooij, and Heiser (2004), and Gifi (1990).”