Nonparametric statistics of dynamic networks with distinguishable nodes

Nonparametric statistics of dynamic networks with distinguishable nodes 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png TEST Springer Journals

Nonparametric statistics of dynamic networks with distinguishable nodes

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Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2017 by Sociedad de Estadística e Investigación Operativa
Subject
Statistics; Statistics, general; Statistical Theory and Methods
ISSN
1133-0686
eISSN
1863-8260
D.O.I.
10.1007/s11749-017-0524-8
Publisher site
See Article on Publisher Site

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