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On multivariate network analysis of statistical data sets with different measures of association

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Abstract

The main goal of the present paper is the development of a general framework of multivariate network analysis of statistical data sets. A general method of multivariate network construction, on the basis of measures of association, is proposed. In this paper we consider Pearson correlation network, sign similarity network, Fechner correlation network, Kruskal correlation network, Kendall correlation network, and the Spearman correlation network. The problem of identification of the threshold graph in these networks is discussed. Different multiple decision statistical procedures are proposed. It is shown that a statistical procedure used for threshold graph identification in one network can be efficiently used for any other network. Our approach allows us to obtain statistical procedures with desired properties for any network.

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Authors and Affiliations

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

    Valery A. Kalyagin & Alexander P. Koldanov

  2. Center for Applied Optimization, University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

Authors
  1. Valery A. Kalyagin
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  2. Alexander P. Koldanov
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  3. Panos M. Pardalos
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Correspondence to Valery A. Kalyagin.

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Kalyagin, V.A., Koldanov, A.P. & Pardalos, P.M. On multivariate network analysis of statistical data sets with different measures of association. Ann Math Artif Intell 76, 83–92 (2016). https://doi.org/10.1007/s10472-015-9464-8

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  • DOI: https://doi.org/10.1007/s10472-015-9464-8

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