Universal Inference adapted via edge sampling yields e-values for finite-sample model selection and hypothesis testing on dependent network data, with proofs of validity and power under alternatives.
27 Proof.We have that Xn = X ij {Pij(ˆθ)−ϱ nPij(θ∗)}= X ij (Aij −EA ij) since Sengupta and Chen (2018) show that A1 is met for the PABM
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Universal Inference for model selection on networks
Universal Inference adapted via edge sampling yields e-values for finite-sample model selection and hypothesis testing on dependent network data, with proofs of validity and power under alternatives.