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Credibility Theory Oriented Sign Test for Imprecise Observations and Imprecise Hypotheses

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Synergies of Soft Computing and Statistics for Intelligent Data Analysis

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 190))

Abstract

This paper extends the sign test to the case where the available observations and underlying hypotheses about the population median are imprecise quantities, rather than crisp. To do this, the associated test statistic is extended, using some elements of credibility theory. Finally, to reject or accept the null hypothesis of interest, we extend the concept of classical p-value.

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Author information

Authors and Affiliations

  1. Dept. of Mathematical Sciences, Isfahan University of Technology, Isfahan, 84156, Iran

    Gholamreza Hesamian & Seyed Mahmood Taheri

Authors
  1. Gholamreza Hesamian
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  2. Seyed Mahmood Taheri
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Corresponding author

Correspondence to Gholamreza Hesamian .

Editor information

Editors and Affiliations

  1. Faculty of Computer Science, Otto-von-Guericke University of Magdebur, Geb. 29, Raum 008, Universitätsplatz 2, Magdeburg, 39106, Germany

    Rudolf Kruse

  2. , FB Informatik & Informationswissenschaft, University of Konstanz, Konstanz, 78457, Germany

    Michael R. Berthold

  3. of Magdeburg, Faculty of Computer Science, Otto-von-Guericke University, Geb. 29, Universitätsplatz 2 008, Magdeburg, 39106, Germany

    Christian Moewes

  4. , Department of Statistics and OR, University of Oviedo, C/ Calvo Sotelo, s/n, Oviedo, 33007, Spain

    María Ángeles Gil

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

    Przemysław Grzegorzewski

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

    Olgierd Hryniewicz

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Hesamian, G., Taheri, S.M. (2013). Credibility Theory Oriented Sign Test for Imprecise Observations and Imprecise Hypotheses. In: Kruse, R., Berthold, M., Moewes, C., Gil, M., Grzegorzewski, P., Hryniewicz, O. (eds) Synergies of Soft Computing and Statistics for Intelligent Data Analysis. Advances in Intelligent Systems and Computing, vol 190. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33042-1_17

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  • DOI: https://doi.org/10.1007/978-3-642-33042-1_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33041-4

  • Online ISBN: 978-3-642-33042-1

  • eBook Packages: EngineeringEngineering (R0)

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