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A Model for Correlated Paired Comparison Data

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Advances in Theoretical and Applied Statistics

Part of the book series: Studies in Theoretical and Applied Statistics ((STASSPSS))

Abstract

Paired comparison data arise when objects are compared in couples. This type of data occurs in many applications. Traditional models developed for the analysis of paired comparison data assume independence among all observations, but this seems unrealistic because comparisons with a common object are naturally correlated. A model that introduces correlation between comparisons with at least a common object is discussed. The likelihood function of the proposed model involves the approximation of a high dimensional integral. To overcome numerical difficulties a pairwise likelihood approach is adopted. The methodology is illustrated through the analysis of the 2006/2007 Italian men’s volleyball tournament and the 2008/2009 season of the Italian water polo league.

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Acknowledgements

The authors would like to thank David Firth for valuable discussion and suggestions.

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Authors and Affiliations

  1. Department of Statistical Sciences, University of Padua, via C. Battisti 241, 35121, Padova, Italy

    Manuela Cattelan

  2. D.A.I.S., University Ca’ Foscari, San Giobbe, Cannaregio 873, 30121, Venice, Italy

    Cristiano Varin

Authors
  1. Manuela Cattelan
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  2. Cristiano Varin
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Corresponding author

Correspondence to Manuela Cattelan .

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Editors and Affiliations

  1. Department of Statistics, University of Trieste, P.le Europa 1, Trieste, 34127, Italy

    Nicola Torelli

  2. Department of Statistics, University of Padua, Via Cesare Battisti 241, Padova, 35121, Italy

    Fortunato Pesarin

  3. Université Paris Descartes, rue des Saints-Pères 45, Paris, 75270, France

    Avner Bar-Hen

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Cattelan, M., Varin, C. (2013). A Model for Correlated Paired Comparison Data. In: Torelli, N., Pesarin, F., Bar-Hen, A. (eds) Advances in Theoretical and Applied Statistics. Studies in Theoretical and Applied Statistics(). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35588-2_16

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