Introduces a nonparametric inference procedure based on a sparse signed graphon model that yields valid confidence intervals for balance parameters and reports strong empirical evidence for balance theory across real signed networks.
The bootstrap and Edgeworth expansion
2 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 2representative citing papers
An approximate inequality for the probability involving order statistics under near-i.i.d. conditions is established and applied to justify resampling-based statistical procedures.
citing papers explorer
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Nonparametric Inference for Balance in Signed Networks
Introduces a nonparametric inference procedure based on a sparse signed graphon model that yields valid confidence intervals for balance parameters and reports strong empirical evidence for balance theory across real signed networks.
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On a Probability Inequality for Order Statistics with Applications to Bootstrap, Conformal Prediction, and more
An approximate inequality for the probability involving order statistics under near-i.i.d. conditions is established and applied to justify resampling-based statistical procedures.