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• W3143609537 abstract "We propose a new class of multiplier bootstraps for count functionals, ranging from a fast, approximate linear bootstrap tailored to sparse, massive graphs to a quadratic bootstrap procedure that offers refined accuracy for smaller, denser graphs. For the fast, approximate linear bootstrap, we show that $sqrt{n}$-consistent inference of the count functional is attainable in certain computational regimes that depend on the sparsity level of the graph. Furthermore, even in more challenging regimes, we prove that our bootstrap procedure offers valid coverage and vanishing confidence intervals. For the quadratic bootstrap, we establish an Edgeworth expansion and show that this procedure offers higher-order accuracy under appropriate sparsity conditions. We complement our theoretical results with a simulation study and real data analysis and verify that our procedure offers state-of-the-art performance for several functionals." @default.
• W3143609537 created "2021-04-13" @default.
• W3143609537 creator A5065384967 @default.
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• W3143609537 date "2020-09-13" @default.
• W3143609537 modified "2023-09-25" @default.
• W3143609537 title "Trading off Accuracy for Speedup: Multiplier Bootstraps for Subgraph Counts" @default.
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• W3143609537 doi "https://doi.org/10.48550/arxiv.2009.06170" @default.
• W3143609537 hasPublicationYear "2020" @default.
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