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Multidimensional Scaling

  • Wolfgang Karl HärdleEmail author
  • Léopold Simar
Chapter

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.

References

  1. W.R. Dillon, M. Goldstein, Multivariate Analysis (Wiley, New York, 1984)zbMATHGoogle Scholar
  2. L. Fahrmeir, A. Hamerle, Multivariate Statistische Verfahren (De Gruyter, Berlin, 1984)zbMATHGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Ladislaus von Bortkiewicz Chair of StatisticsHumboldt-Universität zu BerlinBerlinGermany
  2. 2.Institute of Statistics, Biostatistics and Actuarial SciencesUniversité Catholique de LouvainLouvain-la-NeuveBelgium

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