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Hierarchy Techniques of Multidimensional Data Analysis (MDA) in Social Medicine Research

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Advances in Data Science and Classification

Abstract

Multidimensional Data Analysis (MDA), by means of statistical methods, is used in order to select the main sources of variability and to establish a hierarchy of the characteristics under study. This hierarchy is necessary in social medicine for decision making.

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References

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Author information

Authors and Affiliations

  1. Institute of Public Health, Bucharest, Leonte str., 1-3, 76256, Romania

    Sabina Popescu-Spineni

Authors
  1. Sabina Popescu-Spineni
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Editor information

Editors and Affiliations

  1. Dipartimento di Statisticà, Probabilità e Statistiche Applicate, Università di Roma “La Sapienza”, Piazzale Aldo Moro 5, I-00185, Roma, Italia

    Alfredo Rizzi

  2. Dipartimento di Metodi Quantitativi e Teoria Economica, Università “G. D’Annunzio” di Chieti, Viale Pindaro 42, I-65127, Pescara, Italia

    Maurizio Vichi

  3. Institut für Statistik und Wirtschaftsmathematik, Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen, Wüllnerstraße 3, D-52056, Aachen, Germany

    Hans-Hermann Bock

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© 1998 Springer-Verlag Berlin · Heidelberg

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Popescu-Spineni, S. (1998). Hierarchy Techniques of Multidimensional Data Analysis (MDA) in Social Medicine Research. In: Rizzi, A., Vichi, M., Bock, HH. (eds) Advances in Data Science and Classification. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-72253-0_87

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  • DOI: https://doi.org/10.1007/978-3-642-72253-0_87

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-64641-9

  • Online ISBN: 978-3-642-72253-0

  • eBook Packages: Springer Book Archive

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