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Homogeneity Hypothesis Testing for Degree Distribution in the Market Graph

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Models, Algorithms, and Technologies for Network Analysis (NET 2016)

Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 197))

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Abstract

The problem of homogeneity hypothesis testing for degree distribution in the market graph is studied. Multiple hypotheses testing procedure is proposed and applied for China and India stock markets. The procedure is constructed using bootstrap method for individual hypotheses and Bonferroni correction for multiple testing. It is shown that homogeneity hypothesis of degree distribution for the stock markets for the period of 2003–2014 is not accepted.

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Acknowledgements

The work of Koldanov P.A. was conducted at the Laboratory of Algorithms and Technologies for Network Analysis of National Research University Higher School of Economics. The work is partially supported by RFHR grant 15-32-01052.

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Authors and Affiliations

  1. Laboratory of Algorithms and Technologies for Network Analysis, National Research University Higher School of Economics, Nizhny Novgorod, Russia

    D. P. Semenov & P. A. Koldanov

Authors
  1. D. P. Semenov
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  2. P. A. Koldanov
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Corresponding author

Correspondence to D. P. Semenov .

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Editors and Affiliations

  1. National Research University Higher School of Economics, Nizhny Novgorod, Russia

    Valery A. Kalyagin

  2. National Research University Higher School of Economics, Nizhny Novgorod, Russia

    Alexey I. Nikolaev

  3. Department of Industrial and Systems Engineering, University of Florida, Gainesville, Florida, USA

    Panos M. Pardalos

  4. Department of Industrial Engineering, University of Pittsburgh , Pittsburgh, Pennsylvania, USA

    Oleg A. Prokopyev

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Semenov, D.P., Koldanov, P.A. (2017). Homogeneity Hypothesis Testing for Degree Distribution in the Market Graph. In: Kalyagin, V., Nikolaev, A., Pardalos, P., Prokopyev, O. (eds) Models, Algorithms, and Technologies for Network Analysis. NET 2016. Springer Proceedings in Mathematics & Statistics, vol 197. Springer, Cham. https://doi.org/10.1007/978-3-319-56829-4_11

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