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A mutual information-based k -sample test for discrete distributions

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  • Robert Drake
  • Apratim Guha

Abstract

The two-sample problem and its extension to the k -sample problem are well known in the statistical literature. However, the discrete version of the k -sample problem is relatively less explored. Here in this work we suggest a k -sample non-parametric test procedure for discrete distributions based on mutual information. A detailed power study with comparison with other alternatives is provided. Finally, a comparison of some English soccer league teams based on their goal-scoring pattern is discussed.

Suggested Citation

  • Robert Drake & Apratim Guha, 2014. "A mutual information-based k -sample test for discrete distributions," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(9), pages 2011-2027, September.
    • Handle: RePEc:taf:japsta:v:41:y:2014:i:9:p:2011-2027
    DOI: 10.1080/02664763.2014.899325
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    References listed on IDEAS

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    1. McKeague, Ian W. & Zhao, Yichuan, 2002. "Simultaneous confidence bands for ratios of survival functions via empirical likelihood," Statistics & Probability Letters, Elsevier, vol. 60(4), pages 405-415, December.
    2. Einmahl, J.H.J. & McKeague, I.W., 1999. "Confidence tubes for multiple quantile plots via empirical likelihood," Other publications TiSEM b64493f8-1c01-40fd-b16d-7, Tilburg University, School of Economics and Management.
    3. Jing, Bing-Yi, 1995. "Two-sample empirical likelihood method," Statistics & Probability Letters, Elsevier, vol. 24(4), pages 315-319, September.
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    Cited by:

    1. Rosa Aghdam & Mojtaba Ganjali & Parisa Niloofar & Changiz Eslahchi, 2016. "Inferring gene regulatory networks by an order independent algorithm using incomplete data sets," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(5), pages 893-913, April.

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