Multidimensional Scaling

Wolfgang Karl Härdle () and Leopold Simar
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Wolfgang Karl Härdle: Humboldt-Universität zu Berlin, Ladislaus von Bortkiewicz Chair of Statistics

Chapter Chapter 17 in Applied Multivariate Statistical Analysis, 2019, pp 443-459 from Springer

Abstract: 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.

Date: 2019
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Chapter: Multidimensional Scaling (2024)
Chapter: Multidimensional Scaling (2015)
Chapter: Multidimensional Scaling (2003)
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DOI: 10.1007/978-3-030-26006-4_17

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