Abstract
One major aim of multivariate data analysis is dimension reduction. For data measured in Euclidean coordinates, Factor Analysis and Principal Component Analysis are dominantly used tools. In many applied sciences data is recorded as ranked information. For example, in marketing, one may record “product A is better than product B”. High-dimensional observations therefore often have mixed data characteristics and contain relative information (w.r.t. a defined standard) rather than absolute coordinates that would enable us to employ one of the multivariate techniques presented so far.
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References
Dillon, W. R., & Goldstein, M. (1984). Multivariate analysis. New York: Wiley.
Fahrmeir, L., & Hamerle, A. (1984). Multivariate statistische verfahren. Berlin: De Gruyter.
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Härdle, W.K., Simar, L. (2015). Multidimensional Scaling. In: Applied Multivariate Statistical Analysis. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45171-7_17
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DOI: https://doi.org/10.1007/978-3-662-45171-7_17
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