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Comparisons of Heterogeneity: a Nonparametric Test for the Multisample Case

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mODa 8 - Advances in Model-Oriented Design and Analysis

Part of the book series: Contributions to Statistics ((CONTRIB.STAT.))

Abstract

In several scientific disciplines it is often of interest to compare the concentration of the distribution of a categorical variable between two or more populations. The aim is to establish if the heterogeneities of the distributions are equal or not. We propose a nonparametric solution based on a permutation test. The main properties of the test and a Monte Carlo simulation in order to evaluate its behaviour will be discussed.

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Author information

Authors and Affiliations

  1. Department of Mathematics, University of Ferrara, Via Macchiavelli, 35, 44100, Ferrara, Italy

    Rosa Arboretti Giancristofaro

  2. Center for Modelling, Computing and Statistics, University of Ferrara, Via Macchiavelli, 35, 44100, Ferrara, Italy

    Stefano Bonnini

  3. Department of Statistical Sciences, University of Padova, Via C. Battisti, 241-243, 35121, Padova, Italy

    Fortunato Pesarin

Authors
  1. Rosa Arboretti Giancristofaro
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  2. Stefano Bonnini
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  3. Fortunato Pesarin
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Editor information

Editors and Affiliations

  1. Departamento de Matemáticas, E.T.S.I. Industriales, Universidad de Castilla-La Mancha, Avda. Camilo José Cela, 3, 13071, Ciudad Real, Spain

    Jesús López-Fidalgo

  2. Departamento de Estadística, Facultad de Ciencias, Universidad de Salamanca, Plaza de los Caídos s/n, 37008, Salamanca, Spain

    Juan Manuel Rodríguez-Díaz

  3. Department of Statistics, University of Glasgow, 15 University Gardens, Glasgow, G12 8QW, UK

    Ben Torsney

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© 2007 Physica-Verlag Heidelberg

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Giancristofaro, R.A., Bonnini, S., Pesarin, F. (2007). Comparisons of Heterogeneity: a Nonparametric Test for the Multisample Case. In: López-Fidalgo, J., Rodríguez-Díaz, J.M., Torsney, B. (eds) mODa 8 - Advances in Model-Oriented Design and Analysis. Contributions to Statistics. Physica-Verlag HD. https://doi.org/10.1007/978-3-7908-1952-6_3

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