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The Forward Search for Classical Multidimensional Scaling When the Starting Data Matrix Is Known

  • Nadia SolaroEmail author
  • Massimo Pagani
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

This work provides an extension of the Forward Search to classical multidimensional scaling according to a double perspective: first, as a diagnostic tool in order to detect outlying units and monitor their influences on the main analysis results. Second, as a comparative tool when two or more solutions need to be compared. A case study from a clinical setting is then considered.

Keywords

Procrustes Analysis Forward Search Outlying Unit Initial Subset Relative Eigenvalue 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Atkinson, A. C., Riani, M., & Cerioli, A. (2004). Exploring multivariate data with the forward search. New York: Springer.zbMATHGoogle Scholar
  2. Borg, I., & Groenen, P. J. F. (2005). Modern multidimensional scaling – Theory and applications (2nd edition). New York: Springer.zbMATHGoogle Scholar
  3. Cox, T. F., & Cox, M. A. A. (2001). Multidimensional scaling (2nd edition). New York: Chapman and Hall/CRC.zbMATHGoogle Scholar
  4. Lucini, D., Cusumano, G., Bellia, A., Kozakova, M., Di Fede, G., Lauro, R., Pagani, M., Lauro, R. (2006). Is reduced baroreflex gain a component of the Metabolic Syndrome? Insights from the Linosa Study. Journal of Hypertension, 24, 361–370.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  1. 1.Department of StatisticsUniversity of Milano-BicoccaMilanItaly

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