Papers › Bootstrap estimators for the tail-index and for the count statistics of graphex processes
Bootstrap estimators for the tail-index and for the count statistics of graphex processes
Zacharie Naulet, Daniel M. Roy, Ekansh Sharma, Victor Veitch
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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 \textit{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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