Nonparametric statistics of dynamic networks with distinguishable nodes

• Daniel Fraiman ^[1] ; Nicolas Fraiman ^[3] ; Ricardo Fraiman ^[2]
  1. [1] Universidad de San Andrés

     Universidad de San Andrés

     Argentina

     
  2. [2] Universidad de la República

     Universidad de la República

     Uruguay

     
  3. [3] University of North Carolina

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• Localización: Test: An Official Journal of the Spanish Society of Statistics and Operations Research, ISSN-e 1863-8260, ISSN 1133-0686, Vol. 26, Nº. 3, 2017, págs. 546-573
• Idioma: inglés
• DOI: 10.1007/s11749-017-0524-8
• Texto completo no disponible (Saber más ...)
• Resumen

  • The study of random graphs and networks had an explosive development in the last couple of decades. Meanwhile, techniques for the statistical analysis of sequences of networks were less developed. In this paper, we focus on networks sequences with a fixed number of labeled nodes and study some statistical problems in a nonparametric framework. We introduce natural notions of center and a depth function for networks that evolve in time. We develop several statistical techniques including testing, supervised and unsupervised classification, and some notions of principal component sets in the space of networks. Some examples and asymptotic results are given, as well as two real data examples.