A bias-corrected multiplier bootstrap makes the largest non-spiked bulk eigenvalues asymptotically Gaussian, giving valid confidence intervals for the bulk edge and a threshold-free spike-number estimator.
Nonparametric graphon estimation.arXiv preprint arXiv:1309.5936
2 Pith papers cite this work. Polarity classification is still indexing.
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abstract
We propose a nonparametric framework for the analysis of networks, based on a natural limit object termed a graphon. We prove consistency of graphon estimation under general conditions, giving rates which include the important practical setting of sparse networks. Our results cover dense and sparse stochastic blockmodels with a growing number of classes, under model misspecification. We use profile likelihood methods, and connect our results to approximation theory, nonparametric function estimation, and the theory of graph limits.
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2026 2representative citing papers
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