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- Title
Using the bootstrap for statistical inference on random graphs.
- Authors
Thompson, Mary E.; Ramirez Ramirez, Lilia L.; Lyubchich, Vyacheslav; Gel, Yulia R.
- Abstract
In this paper we propose a new nonparametric approach to network inference that may be viewed as a fusion of block sampling procedures for temporally and spatially dependent processes with the classical network methodology. We develop estimation and uncertainty quantification procedures for network mean degree using a 'patchwork' sample and nonparametric bootstrap, under the assumption of unknown degree distribution. We provide a heuristic justification of asymptotic properties of the proposed 'patchwork' sampling and present cross-validation methodology for selecting an optimal 'patch' size. We validate the new 'patchwork' bootstrap on simulated networks with short- and long-tailed mean degree distributions, and revisit the Erdös collaboration data to illustrate the proposed methodology. The Canadian Journal of Statistics 44: 3-24; 2016 © 2015 Statistical Society of Canada
- Subjects
INFERENTIAL statistics; RANDOM graphs; STATISTICAL bootstrapping; STATISTICAL sampling; ASYMPTOTIC distribution
- Publication
Canadian Journal of Statistics, 2016, Vol 44, Issue 1, p3
- ISSN
0319-5724
- Publication type
Article
- DOI
10.1002/cjs.11271