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A distance-based variety of nonlinear multivariate data analysis, including weights for objects and variables

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  • Jacques Commandeur
  • Patrick Groenen
  • Jacqueline Meulman

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Suggested Citation

  • Jacques Commandeur & Patrick Groenen & Jacqueline Meulman, 1999. "A distance-based variety of nonlinear multivariate data analysis, including weights for objects and variables," Psychometrika, Springer;The Psychometric Society, vol. 64(2), pages 169-186, June.
    • Handle: RePEc:spr:psycho:v:64:y:1999:i:2:p:169-186
    DOI: 10.1007/BF02294534
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    References listed on IDEAS

    as
    1. Jacqueline Meulman & Peter Verboon, 1993. "Points of view analysis revisited: Fitting multidimensional structures to optimal distance components with cluster restrictions on the variables," Psychometrika, Springer;The Psychometric Society, vol. 58(1), pages 7-35, March.
    2. J. Kruskal, 1964. "Nonmetric multidimensional scaling: A numerical method," Psychometrika, Springer;The Psychometric Society, vol. 29(2), pages 115-129, June.
    3. J. Kruskal, 1964. "Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis," Psychometrika, Springer;The Psychometric Society, vol. 29(1), pages 1-27, March.
    4. Jacqueline Meulman, 1992. "The integration of multidimensional scaling and multivariate analysis with optimal transformations," Psychometrika, Springer;The Psychometric Society, vol. 57(4), pages 539-565, December.
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    Cited by:

    1. Beibei Yuan & Willem Heiser & Mark Rooij, 2019. "The δ-Machine: Classification Based on Distances Towards Prototypes," Journal of Classification, Springer;The Classification Society, vol. 36(3), pages 442-470, October.

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