Multidimensional Scaling

Wolfgang Härdle () and Léopold Simar ()
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Wolfgang Härdle: Humboldt-Universität zu Berlin, CASE — Center for Applied Statistics and Economics, Institut für Statistik und Ökonometrie
Léopold Simar: Université Catholique Louvain, Inst. Statistique

Chapter 15 in Applied Multivariate Statistical Analysis, 2003, pp 373-392 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.

Keywords: Distance Matrix; Multidimensional Scaling; Initial Configuration; Positive Semidefinite; Nonmetric Multidimensional Scaling (search for similar items in EconPapers)
Date: 2003
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DOI: 10.1007/978-3-662-05802-2_15

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