Using bootstrap for statistical inference on random graphs
| Autor: | Lilia L. Ramirez Ramirez, Yulia R. Gel, Vyacheslav Lyubchich, Mary E. Thompson |
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| Rok vydání: | 2014 |
| Předmět: |
Statistics and Probability
Random graph FOS: Computer and information sciences Computer science Nonparametric statistics Sampling (statistics) Inference Sample (statistics) 0102 computer and information sciences Degree distribution 01 natural sciences Statistics - Applications 010104 statistics & probability 010201 computation theory & mathematics Statistical inference Applications (stat.AP) 0101 mathematics Statistics Probability and Uncertainty Uncertainty quantification Algorithm |
| DOI: | 10.48550/arxiv.1402.3647 |
| Popis: | In this paper, we propose 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 investigate asymptotic properties of the proposed patchwork bootstrap procedure 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 Erdos collaboration data to illustrate the proposed methodology. Comment: The paper has been withdrawn by the authors: a general revision of methodology is needed |
| Databáze: | OpenAIRE |
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