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2021 Bootstrap estimators for the tail-index and for the count statistics of graphex processes
Zacharie Naulet, Daniel M Roy, Ekansh Sharma, Victor Veitch
Electron. J. Statist. 15(1): 282-325 (2021). DOI: 10.1214/20-EJS1789

Abstract

Graphex processes resolve some pathologies in traditional random graph models, notably, providing models that are both projective and allow sparsity. Most of the literature on graphex processes study them from a probabilistic point of view. Techniques for inferring the parameter of these processes – the so-called graphon – are still marginal; exceptions are a few papers considering parametric families of graphons. Nonparametric estimation remains unconsidered. In this paper, we propose estimators for a selected choice of functionals of the graphon. Our estimators originate from the subsampling theory for graphex processes, hence can be seen as a form of bootstrap procedure.

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Zacharie Naulet. Daniel M Roy. Ekansh Sharma. Victor Veitch. "Bootstrap estimators for the tail-index and for the count statistics of graphex processes." Electron. J. Statist. 15 (1) 282 - 325, 2021. https://doi.org/10.1214/20-EJS1789

Information

Received: 1 April 2019; Published: 2021
First available in Project Euclid: 6 January 2021

Digital Object Identifier: 10.1214/20-EJS1789

Subjects:
Primary: 62F10
Secondary: 60G55, 60G70

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Vol.15 • No. 1 • 2021
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