Nonparametric statistics of dynamic networks with distinguishable nodes

Daniel Fraiman (), Nicolas Fraiman () and Ricardo Fraiman ()
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Daniel Fraiman: Universidad de San Andrés
Nicolas Fraiman: University of North Carolina
Ricardo Fraiman: Universidad de la República

TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, 2017, vol. 26, issue 3, No 5, 546-573

Abstract: Abstract 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.

Keywords: Depth; Graph estimation; Cluster analysis of graphs; Principal Components; 62H25; 62H30; 05C80; 62G05 (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s11749-017-0524-8

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