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Sugeno Integral-Based Confidence Intervals for the Theoretical h-Index

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Strengthening Links Between Data Analysis and Soft Computing

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 315))

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Abstract

Sugeno integral-based confidence intervals for the theoretical h-index of a fixed-length sequence of i.i.d. random variables are derived. They are compared with other estimators of such a distribution characteristic in a Pareto i.i.d. model. It turns out that in the first case we obtain much wider intervals. It seems to be due to the fact that a Sugeno integral, which may be applied on any ordinal scale, is known to ignore too much information from cardinal-scale data being aggregated.

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Author information

Authors and Affiliations

  1. Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, 01-447, Warsaw, Poland

    Marek Gagolewski

  2. Faculty of Mathematics and Information Science, Warsaw University of Technology, ul. Koszykowa 75, 00-662, Warsaw, Poland

    Marek Gagolewski

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  1. Marek Gagolewski
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Correspondence to Marek Gagolewski .

Editor information

Editors and Affiliations

  1. Polish Academy of Sciences, Faculty of Mathematics and Information Sciences, Systems Research Institute and Warsaw University of Technology, Warsaw, Poland

    Przemyslaw Grzegorzewski

  2. Polish Academy of Sciences, Faculty of Mathematics and Information Sciences, Systems Research Institute and Warsaw University of Technology, Warsaw, Poland

    Marek Gagolewski

  3. Polish Academy of Sciences Systems Research Institute, Warsaw, Poland

    Olgierd Hryniewicz

  4. Departamento de Estadistica e I.O. y D.M., University of Oviedo, Oviedo, Spain

    María Ángeles Gil

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Gagolewski, M. (2015). Sugeno Integral-Based Confidence Intervals for the Theoretical h-Index. In: Grzegorzewski, P., Gagolewski, M., Hryniewicz, O., Gil, M. (eds) Strengthening Links Between Data Analysis and Soft Computing. Advances in Intelligent Systems and Computing, vol 315. Springer, Cham. https://doi.org/10.1007/978-3-319-10765-3_28

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  • DOI: https://doi.org/10.1007/978-3-319-10765-3_28

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-10764-6

  • Online ISBN: 978-3-319-10765-3

  • eBook Packages: EngineeringEngineering (R0)

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