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On the Analysis of Fuzzy Life Times and Quality of Life Data

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Probability, Statistics and Modelling in Public Health

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Summary

Life times, health data, and general quality of life data are often not adequately represented by precise numbers or classes. Such data are called nonprecise or fuzzy, because their quantitative characterization is possible by so-called non-precise numbers. To analyze such data a more general concept than fuzzy numbers from the theory of fuzzy sets is necessary. A suitable concept are so-called non-precise numbers. Generalized methods to analyze such data are available, and basic methods for that are described in the paper.

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References

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Author information

Authors and Affiliations

  1. Vienna University of Technology, 1040, Wien, Austria

    Reinhard Viertl

Authors
  1. Reinhard Viertl
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Editor information

Editors and Affiliations

  1. Université Victor Segalin Bordeaux 2, France

    Mikhail Nikulin  & Daniel Commenges  & 

  2. V. Steklov Mathematical Institute, Saint Petersburg, Russia

    Mikhail Nikulin

  3. Université René Descartes, Paris, France

    Catherine Huber

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© 2006 Springer Science+Business Media, Inc.

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Viertl, R. (2006). On the Analysis of Fuzzy Life Times and Quality of Life Data. In: Nikulin, M., Commenges, D., Huber, C. (eds) Probability, Statistics and Modelling in Public Health. Springer, Boston, MA. https://doi.org/10.1007/0-387-26023-4_30

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